A flow field reconstruction method based on test point position optimization and multi-source data fusion and related device

Abstract

The application belongs to the field of turbine test, and discloses a flow field reconstruction method based on test measuring point position optimization and multi-source data fusion and a related device, which comprises the following steps: establishing a CFD data set and a sparse measuring point test data set; performing POD decomposition on the CFD data set; obtaining correction modal coefficients by using a Gappy POD algorithm according to the sparse measuring point test data set; obtaining Gappy POD reconstructed flow field parameters according to the POD mode and the correction modal coefficients; taking the minimum error of the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters as the target, obtaining optimal measuring point positions; obtaining high-fidelity discrete test data based on the optimal measuring point positions, and obtaining optimization modal coefficients by using the Gappy POD algorithm; and obtaining the flow field reconstruction result of the turbine blade according to the POD mode and the optimization modal coefficients.

Description

Technical Field

[0001] This invention belongs to the field of turbine testing technology, and relates to the field of CFD multi-source data flow field reconstruction. In particular, it relates to a flow field reconstruction method and related device based on test measurement point location optimization and multi-source data fusion. Background Technology

[0002] As a core component of aero-engines, the turbine's performance directly affects the overall efficiency, stability, and service life of the system. Therefore, predicting and analyzing the refined flow field of the turbine is of great significance. However, the complex three-dimensional flows inside the turbine, including eddies, turbulence, and aerodynamic separation, pose certain challenges to existing experimental measurement methods and flow field assessment techniques such as Computational Fluid Dynamics (CFD). For traditional research paradigms, CFD methods can relatively easily obtain global flow field information of complex turbine structures, but due to limitations in simulation accuracy caused by computational resources and model simplification, it is usually difficult to fully and realistically reflect the complex flow characteristics inside the turbine. In contrast, wind tunnel-based experimental measurement methods can realistically reflect the flow phenomena inside the turbine, but are limited by the accuracy of the experimental equipment and the arrangement of measurement points, and the experimental data contains varying degrees of noise, resulting in the spatial distribution of the experimental data not fully reflecting the global flow field.

[0003] Currently, in existing turbine flow field studies, experimental and CFD multi-source data often exist in a shallow exchange of mutual verification, failing to uncover the potential correlation between the two and thus hindering the realization of refined reconstruction of the turbine's internal flow field. Furthermore, the strong potential correlation between experimental and CFD data, coupled with their spatial mismatch, makes it difficult to fuse flow field information such as velocity and pressure fields, represented by high-dimensional data structures like matrices and tensors, using simple methods. Therefore, how to achieve complementary advantages between experimental and CFD data to quickly obtain a refined turbine flow field with higher spatial resolution and data fidelity has become a pressing issue in current turbine flow field measurements. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a flow field reconstruction method and related device based on the optimization of experimental measurement point locations and the fusion of multi-source data. This solves the technical problem that in existing turbine flow field research, experimental / CFD multi-source data are often in a shallow communication state of mutual verification, which cannot explore the potential correlation characteristics between the two, and thus cannot achieve fine reconstruction of the internal flow field of the turbine.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion, comprising: The design space variables of the turbine blade under study are sampled to obtain sampled data. CFD simulation calculations are performed on the sampled data to obtain global low-fidelity flow field parameters; a CFD dataset is established based on the global low-fidelity flow field parameters; and test data under wind tunnel testing of the turbine blade to be studied is collected based on the predetermined uniform measurement point locations to obtain a sparse measurement point test dataset. The CFD dataset is subjected to POD decomposition to obtain POD modes and mode coefficients; Based on the sparse measurement point test dataset, the modal coefficients are corrected using the Gappy POD algorithm to obtain the corrected modal coefficients; Based on the POD mode and the correction mode coefficients, the Gappy POD reconstructed flow field parameters are obtained; Using the measurement point location as the design variable, and with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, the design variable is optimized to obtain the optimal measurement point location. Based on the optimal measurement point location, test data under the wind tunnel test of the turbine blade to be studied are collected to obtain high-fidelity discrete test data; Based on the high-fidelity discrete experimental data, the modal coefficients are corrected using the Gappy POD algorithm to obtain optimized modal coefficients; Based on the POD mode and the optimized mode coefficients, the flow field reconstruction results of the turbine blade under study are obtained.

[0006] Furthermore, the design space variables of the turbine blade to be studied include the exit isentropic Mach number, inlet angle of attack, inlet turbulence intensity, and relative axial pitch of the turbine blade to be studied.

[0007] Furthermore, the process of collecting test data from wind tunnel tests on the turbine blades under study, based on pre-determined uniform measurement point locations, to obtain a sparse measurement point test dataset includes: Determine the wind tunnel test conditions for the turbine blade under study under initial sampling; wherein, the wind tunnel test conditions for the turbine blade under study under initial sampling include the pre-determined uniform measurement point locations; Based on the wind tunnel test conditions of the turbine blade under initial sampling, test data under the wind tunnel test of the turbine blade under study were collected to obtain a sparse measurement point test dataset.

[0008] Furthermore, based on the sparse measurement point test dataset and combined with the POD modes, the process of obtaining the corrected mode coefficients using the Gappy POD algorithm includes: Based on the sparse measurement point test dataset and the POD mode, a modal coefficient optimization formula is constructed; The modal coefficient optimization formula is solved using the least squares method to obtain the corrected modal coefficients.

[0009] Furthermore, the modal coefficient optimization formula is as follows:

[0010]

[0011] in, To correct the modal coefficients; The data consists of discrete test measurement points; These are the modal coefficients; It is a POD mode; The modal order; For the first Test data at each measuring point; This represents the total number of measurement points.

[0012] Furthermore, the process of optimizing the design variables, with the measurement point location as the design variable and minimizing the error between the global low-fidelity flow field parameters and the GappyPOD reconstructed flow field parameters as the objective, to obtain the optimal measurement point location, includes: Determine the number of measuring points; The initial values ​​of the measuring points are determined based on the number of measuring points; where the initial values ​​of the measuring points are several equally spaced measuring points on the turbine blade under study from the leading edge to the trailing edge. Based on the initial value of the measurement point location, with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, and with the measurement point location as the design variable, an objective function is established. Based on a global optimization algorithm, the objective function is optimized to obtain the optimal measurement point location.

[0013] This invention also provides a flow field reconstruction system based on experimental measurement point location optimization and multi-source data fusion, comprising: The data sampling module is used to sample the design space variables of the turbine blade under study and obtain sampled data samples. The dataset establishment module is used to perform CFD simulation calculations on the sampled data to obtain global low-fidelity flow field parameters; based on the global low-fidelity flow field parameters, a CFD dataset is established; based on the predetermined uniform measurement point positions, test data under the wind tunnel test of the turbine blade to be studied is collected to obtain a sparse measurement point test dataset. The POD flow field feature extraction module is used to perform POD decomposition on the CFD dataset to obtain POD modes and mode coefficients. The initial sampling coefficient correction module is used to correct the modal coefficients based on the sparse measurement point test dataset using the Gappy POD algorithm to obtain the corrected modal coefficients. The initial sampling flow field reconstruction module is used to obtain the GappyPOD reconstructed flow field parameters based on the POD mode and the correction mode coefficients; The measurement point optimization module is used to optimize the design variables with the measurement point location as the design variable and the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, so as to obtain the optimal measurement point location. The test module is used to collect test data under wind tunnel testing of the turbine blade under study based on the optimal measurement point position, and obtain high-fidelity discrete test data. An optimized sampling coefficient correction module is used to correct the modal coefficients based on the high-fidelity discrete experimental data using the Gappy POD algorithm to obtain optimized modal coefficients. The optimized sampling flow field reconstruction module is used to obtain the flow field reconstruction results of the turbine blade under study based on the POD mode and the optimized mode coefficients.

[0014] This invention also provides a flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, performs the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion as described in any one of claims 1-6.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion.

[0016] The present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion. Based on the Gappy POD algorithm, it efficiently couples multi-source data from experimental data and CFD data and incorporates them into the optimization strategy of experimental measurement point locations to achieve refined reconstruction of the global flow field distribution of turbine blades, thereby enabling refined acquisition of global flow field information under experimental conditions. Specifically, the POD decomposition method is used to extract flow field features from the global low-fidelity flow field parameters in the CFD dataset, efficiently extracting key modes in the entire spatial physical field. Secondly, the Gappy POD algorithm is used, combined with sparse measurement point experimental datasets, to correct the modal coefficients. Combining the corrected modal coefficients with the POD modes yields the Gappy POD reconstructed flow field parameters, effectively uncovering the close relationship between different source data from experimental and CFD datasets, allowing for complementary advantages among multi-source data, and ultimately obtaining a high-precision, high-spatial-resolution reconstructed flow field, laying a solid foundation for accurate analysis of turbine aerodynamic performance. Furthermore, based on the global low-fidelity flow field parameters and the Gappy POD algorithm... With the goal of minimizing the error between the flow field parameters reconstructed by POD, the optimal spatial distribution scheme of the test measurement points is determined, thereby achieving a refined reconstruction of the flow field with a lower sampling cost. Furthermore, the sparse measurement point layout optimization strategy is combined with the multi-source data fusion method to capture more refined flow field features with fewer test measurement points by optimizing the layout of the test measurement points.

[0018] The flow field reconstruction system, flow field reconstruction device, computer-readable storage medium, and computer program product based on experimental measurement point location optimization and multi-source data fusion provided by this invention possess all the advantages of the aforementioned flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The flowchart shows the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion provided in Example 1. Figure 2 This is a schematic diagram illustrating the principle of turbine blade flow field reconstruction using multi-source data fusion in Example 1. Figure 3 This is a schematic diagram showing the layout of the measuring points before and after optimization in Example 1; Figure 4This is a schematic diagram of the flow field reconstruction results after optimizing the measurement point locations in Example 1; where, Figure 4 a is a schematic diagram of the flow field reconstruction results at 10 measurement points. Figure 4 b is a schematic diagram of the flow field reconstruction results at 15 measurement points; Figure 5 This is a structural block diagram of the flow field reconstruction system based on experimental measurement point location optimization and multi-source data fusion provided in Example 2; Figure 6 The diagram shows the structural block diagram of the flow field reconstruction device based on the optimization of test measurement point locations and the fusion of multi-source data provided in Example 3. Detailed Implementation

[0021] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] Example 1 As attached Figure 1 As shown, this embodiment 1 provides a flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion, including the following steps: Step 1: Sample the design space variables of the turbine blade to be studied to obtain sampled data.

[0023] Specifically, the process of sampling the design space variables of the turbine blade under study to obtain sampled data includes: Step 1-1: Determine the design space variables of the turbine blade under study, and determine the numerical variation range of each design space variable; wherein, the design space variables of the turbine blade under study include the exit isentropic Mach number, inlet angle of attack, inlet turbulence intensity and relative axial pitch of the turbine blade under study.

[0024] Steps 1-2: The Latin hypercube sampling method is used to sample the design space variables of the turbine blade under study to obtain sampled data.

[0025] Step 2: Perform CFD (Computational Fluid Dynamics) simulation on the sampled data to obtain global low-fidelity flow field parameters; establish a CFD dataset based on the global low-fidelity flow field parameters; collect test data under wind tunnel test of the turbine blade to be studied based on the predetermined uniform measurement point positions to obtain a sparse measurement point test dataset.

[0026] The process of collecting test data from wind tunnel tests on the turbine blades under study, based on predetermined uniform measurement point locations, to obtain a sparse measurement point test dataset is as follows: The wind tunnel test conditions of the turbine blade under study under the initial sampling are determined. The wind tunnel test conditions of the turbine blade under study under the initial sampling include the predetermined uniform measurement point positions. The wind tunnel test is carried out on the turbine blade under study. Then, according to the predetermined uniform measurement point positions, the test data of the turbine blade under study under the wind tunnel test are collected to obtain the sparse measurement point test dataset.

[0027] Step 3: Perform POD decomposition (Proper Orthogonal Decomposition) on the CFD dataset to obtain POD modes and modal coefficients. Specifically, performing POD decomposition on the CFD dataset involves extracting POD flow field features from the CFD dataset to obtain POD modes and modal coefficients. The process is as follows: The POD (Orthogonal Eigenvalue Decomposition) model, also known as intrinsic orthogonal decomposition, is used to extract features from the CFD dataset, thereby decomposing the full-space physical field contained in the global CFD flow field data into a set of orthogonal basis vectors. .

[0028] Based on the singular value method, for orthogonal basis vectors Perform decomposition to complete the POD decomposition operation; among which, for orthogonal basis vectors Solve for singular values ​​using elements in the set, and select the elements whose sum of energy values ​​is greater than 99.99%. Truncate the first orthogonal vector and use the first... The orthogonal vectors of order 1 approximate the flow field vectors in space, thereby extracting the global flow field features and obtaining the POD modes and mode coefficients.

[0029] Step 4: Based on the sparse measurement point test dataset, the modal coefficients are corrected using the Gappy POD algorithm to obtain the corrected modal coefficients. The process of obtaining the corrected modal coefficients includes: Step 4-1: Based on the sparse measurement point test dataset and the POD mode, construct the modal coefficient optimization formula.

[0030] Step 4-2: Solve the modal coefficient optimization formula using the least squares method to obtain the corrected modal coefficients.

[0031] Specifically, in Example 1, based on the flow field characteristics and sparse flow field point data in the sparse measurement point test dataset, a regression model is used to establish the relationship between modal sparsity and sparse flow field point data in order to correct low-fidelity modal coefficients, as shown in the attached figure. Figure 2 As shown; specifically, the process of correcting low-fidelity modal coefficients is as follows: The spatial flow field obtained through POD decomposition, i.e., the POD modes, can be approximately represented as a set of orthogonal basis vectors with high spatial resolution characteristics. The linear summation, i.e. ;in, This refers to the spatial flow field, i.e., the POD mode; These are the modal coefficients.

[0032] The sparse measurement point test dataset is defined as a vector group. ;in, For the first Test data at each measuring point; This represents the total number of measurement points.

[0033] Based on the Gappy POD algorithm, the process of correcting low-fidelity modal coefficients can be transformed into an optimization problem, namely, constructing a modal coefficient optimization formula; specifically, the modal coefficient optimization formula is as follows:

[0034] in, To correct the modal coefficients; The data consists of discrete test measurement points; These are the modal coefficients; It is a POD mode; The modal order; For the first Test data at each measuring point; This represents the total number of measurement points.

[0035] Finally, the least squares method is used to solve the optimization formula for the modal coefficients to obtain the corrected modal coefficients.

[0036] Step 5: Obtain the Gappy POD reconstructed flow field parameters based on the POD modes and the corrected mode coefficients. Specifically, the orthogonal basis, i.e., the global low-fidelity flow field parameters obtained through CFD simulation calculations, is combined with the corrected mode coefficients to obtain high-precision, high-spatial-resolution reconstructed flow field data across the entire field, thus obtaining the Gappy POD reconstructed flow field parameters.

[0037] Step 6: Using the measurement point location as the design variable, optimize the design variables with the objective of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters to obtain the optimal measurement point location. Specifically, using the measurement point location as the design variable, and minimizing the flow field reconstruction error obtained from the reduced-order model and the regression model as the objective function, a global optimization algorithm is used to optimize the sparse experimental measurement point layout to obtain the optimal measurement point location.

[0038] Specifically, the process of obtaining the optimal measurement point location includes: Step 6-1: Select the measurement point locations for the experiment as design variables and determine the number of measurement point locations.

[0039] Step 6-2: Determine the initial value of the measuring point position based on the number of measuring point positions; wherein, the initial value of the measuring point position is a number of equally spaced measuring point positions from the leading edge to the trailing edge of the turbine blade under study; specifically, after determining the number of measuring point positions, select a number of equally spaced measuring point positions from the leading edge to the trailing edge of the turbine blade under study as the initial value to obtain the initial value of the measuring point position.

[0040] Step 6-3: Based on the initial value of the measurement point location, with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, and with the measurement point location as the design variable, establish an objective function.

[0041] Step 6-4: Based on the global optimization algorithm, perform a rapid walk on the objective function to obtain the optimal measurement point position.

[0042] Step 7: Based on the optimal measurement point location, collect the test data under the wind tunnel test of the turbine blade to be studied to obtain high-fidelity discrete test data.

[0043] Step 8: Based on the high-fidelity discrete experimental data, the modal coefficients are corrected using the Gappy POD algorithm to obtain optimized modal coefficients. It should be noted that the process of correcting the modal coefficients using the Gappy POD algorithm based on the high-fidelity discrete experimental data to obtain optimized modal coefficients is basically the same as the process of obtaining corrected modal coefficients in Step 4 above, and will not be repeated here.

[0044] Step 9: Based on the POD mode and the optimized mode coefficients, obtain the flow field reconstruction results of the turbine blade under study.

[0045] In this embodiment 1, the Gappy POD method is used to achieve efficient coupling of experimental data and CFD multi-source data, completing a refined reconstruction of the global flow field distribution of the turbine. This allows for more precise acquisition of global flow field information under experimental conditions. Specifically, POD decomposition is performed on the CFD dataset with global low-fidelity flow field parameters to extract flow field features and efficiently extract key modes in the full-space physical field. Simultaneously, the Gappy POD method is used to obtain the corrected modal coefficients under experimental conditions using discrete high-fidelity experimental measurement point data. Subsequently, the corrected modal coefficients are combined with CFD flow field features to obtain a refined flow field distribution. This invention can effectively uncover the close relationship between different sources of experimental data and CFD data, allowing for complementary advantages among multiple sources, thereby obtaining a high-precision, high-spatial-resolution reconstructed flow field. This lays a solid foundation for the accurate analysis of turbine aerodynamic performance and has significant engineering practical value.

[0046] In this embodiment 1, the measurement point location is optimized with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters. This obtains the optimal measurement point location, further combining the sparse measurement point layout optimization strategy with the multi-source data fusion method. By optimizing the experimental measurement point layout, more refined flow field features can be captured with fewer experimental measurement points. Specifically, based on the global optimization algorithm, an objective function is constructed in conjunction with the Gappy POD method, with the goal of minimizing the global error of the reconstructed flow field after multi-source data fusion. Within this framework, the optimal spatial distribution scheme of the experimental measurement points is determined through the optimization algorithm, thereby achieving refined reconstruction of the flow field with a lower sampling cost.

[0047] The flow field reconstruction method based on the optimization of test measurement point locations and the fusion of multi-source data described in Example 1 effectively reduces the time and economic costs required in the process of arranging test measurement points, and significantly improves the reliability and spatial resolution of the reconstructed flow field. In addition, while ensuring high-fidelity reconstruction, it provides theoretical support and technical guarantee for the efficient analysis of complex turbine aerodynamic flow fields, and has important engineering practical value.

[0048] Example explanation: Taking the second-stage rotor blade of a PW four-stage low-pressure turbine as an example, the flow field reconstruction method based on the optimization of test measurement point positions and the fusion of multi-source data described above will be explained in detail as follows: (1) Determine the research object and operating parameters and perform sampling. The outlet isentropic Mach number, inlet angle of attack, and inlet turbulence intensity of the second-stage rotor blade of a four-stage low-pressure turbine in PW were selected as design space variables. For each design space variable, a corresponding range of variation was selected; specifically, the outlet isentropic Mach number ranged from 0.7 to 1.2, the angle of attack ranged from -10° to 5°, and the inlet turbulence intensity ranged from 0.01 to 0.1. Two hundred sets of data samples were collected within the design space using the Latin hypercube sampling method.

[0049] (2) Establish CFD dataset and sparse measurement point test dataset Based on the sampled data, CFX (a commercial software for computational fluid dynamics analysis) was used for simulation to obtain the full-space low-fidelity flow field parameters of the relevant samples, i.e., the global low-fidelity flow field parameters; based on the global low-fidelity flow field parameters, a CFD dataset was established.

[0050] A subset of samples was randomly selected from the CFD dataset, and random noise was added to simulate the generation of experimental data. The simulated experimental data was then used to construct a sparse measurement point experimental dataset. The noise amplitude was 1% and 5% of the original data of the selected subset of samples. It should be noted that the measurement point positions were selected at equal intervals from the leading edge to the trailing edge, and the number of measurement points was set to 20. After processing, high-fidelity measurement point data simulating the real experiment was obtained, and a sparse measurement point experimental dataset was established.

[0051] (3) Obtaining flow field parameters for Gappy POD reconstruction Based on the constructed CFD dataset, a flow field order reduction model, namely intrinsic orthogonal decomposition (POD), is used to process the CFD dataset. The physical field of the entire space contained in the CFD dataset is decomposed into a set of orthogonal basis vectors. Based on singular value decomposition, the first r-order orthogonal vectors with a sum of energy values ​​greater than 99.99% are truncated, and finally the first r-order orthogonal vectors are used to approximate the flow field vectors in space, thus completing the extraction of global flow field features.

[0052] The spatial flow field obtained through the POD method can be approximated as a linear sum of a set of orthogonal basis vectors with high spatial resolution characteristics, i.e. The vector set obtained through the experiment is assumed to be... Based on the GappyPOD method, solving for the corrected orthogonal basis coefficients can be transformed into an optimization problem, i.e. Then, the least squares method is used to solve for the optimal orthogonal basis coefficients, i.e., the corrected mode coefficients. Next, the orthogonal basis obtained from the CFD data is combined with the corrected mode coefficients to obtain high-precision, high-spatial-resolution reconstructed flow field data, i.e., the Gappy POD reconstructed flow field parameters.

[0053] (4) Optimization of measuring point location Using the measurement point location as the design variable, the design variable optimization is carried out with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters. The sparse test measurement point layout optimization is carried out in combination with the global optimization algorithm to obtain the optimal side point location. Among them, after determining the number of test measurement points, the equidistant measurement point locations from the leading edge to the trailing edge are selected as the initial values.

[0054] (5) High-precision reconstruction of flow field by multi-source data fusion Based on the optimal measurement point location, experimental data from wind tunnel tests on the turbine blade under study are collected to obtain high-fidelity discrete experimental data. Then, the experimental / CFD data are coupled using the Gappy POD method to complete a high-precision reconstruction of the turbine flow field. Specifically, based on the high-fidelity discrete experimental data, the modal coefficients are corrected using the Gappy POD algorithm to obtain optimized modal coefficients. Based on the POD modes and the optimized modal coefficients, the flow field reconstruction results of the turbine blade under study are obtained.

[0055] As attached Figure 3 As shown, attached Figure 3 The attached diagram shows the layout of the measuring points before and after optimization in Example 1; from the attached diagram... Figure 3 As can be seen, when the number of test measurement points is 10, the optimized average relative error is 1.019%, and the maximum relative error is 3.159%. Compared with the flow field reconstruction results of uniform arrangement before optimization, the average relative error and the maximum relative error have decreased by 0.728% and 0.578%, respectively. When the number of test measurement points is 15, the optimized average relative error and the maximum relative error are 1.010% and 0.578%, respectively. Compared with the flow field reconstruction results of uniform arrangement before optimization, the average relative error and the maximum relative error have decreased by 0.192% and 0.070%, respectively. This verifies the correctness and effectiveness of the high-precision flow field reconstruction based on sparse measurement point location optimization.

[0056] As attached Figure 4 As shown, attached Figure 4 The diagram below shows the flow field reconstruction results after optimizing the measurement point locations in Example 1; where, Figure 4 a is a schematic diagram of the flow field reconstruction results at 10 measurement points. Figure 4 b is a schematic diagram of the flow field reconstruction results at 15 measuring points; from the attached diagram... Figure 4As can be seen, after optimization, the increased number of test measurement points near the load peak (i.e., near the blade throat) improves the accuracy of the load distribution reconstruction results near the load peak. Adding measurement points to the leading edge increases the flow field information at the leading edge of the airfoil, thereby improving the accuracy of the flow field reconstruction. Therefore, the proposed multi-source data flow field reconstruction method based on optimized test measurement point locations can improve the accuracy of load distribution reconstruction without increasing the number of test measurement points.

[0057] Example 2 As attached Figure 5 As shown, this embodiment 2 provides a flow field reconstruction system based on experimental measurement point location optimization and multi-source data fusion, including a data sampling module, a dataset establishment module, a POD flow field feature extraction module, an initial sampling coefficient correction module, an initial sampling flow field reconstruction module, a measurement point optimization module, an experimental module, an optimized sampling coefficient correction module, and an optimized sampling flow field reconstruction module.

[0058] The system comprises the following modules: a data sampling module for sampling the design space variables of the turbine blade under study to obtain sampled data; a dataset establishment module for performing CFD simulation calculations on the sampled data to obtain global low-fidelity flow field parameters; establishing a CFD dataset based on the global low-fidelity flow field parameters; collecting test data from wind tunnel tests of the turbine blade under study based on predetermined uniform measurement point locations to obtain a sparse measurement point test dataset; a POD flow field feature extraction module for performing POD decomposition on the CFD dataset to obtain POD modes and modal coefficients; an initial sampling coefficient correction module for correcting the modal coefficients using the Gappy POD algorithm based on the sparse measurement point test dataset to obtain corrected modal coefficients; an initial sampling flow field reconstruction module for obtaining Gappy POD reconstructed flow field parameters based on the POD modes and the corrected modal coefficients; and a measurement point optimization module for optimizing the flow field using the measurement point location as a design variable and the global low-fidelity flow field parameters and the Gappy POD algorithm. The design variables are optimized to minimize the error between the POD reconstructed flow field parameters to obtain the optimal measurement point location. The experimental module collects test data from wind tunnel tests of the turbine blade under study based on the optimal measurement point location, obtaining high-fidelity discrete test data. The optimized sampling coefficient correction module corrects the modal coefficients using the Gappy POD algorithm based on the high-fidelity discrete test data, obtaining optimized modal coefficients. The optimized sampling flow field reconstruction module obtains the flow field reconstruction results of the turbine blade under study based on the POD modes and the optimized modal coefficients.

[0059] Example 3 As attached Figure 3As shown, this embodiment 3 provides a flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion, including: a memory for storing computer programs; and a processor for executing the computer program to implement the steps of the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion.

[0060] When the processor executes the computer program, it implements the steps of the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion, for example: The design space variables of the turbine blade under study are sampled to obtain sampled data samples; CFD simulation calculations are performed on the sampled data samples to obtain global low-fidelity flow field parameters; a CFD dataset is established based on the global low-fidelity flow field parameters; test data under wind tunnel tests of the turbine blade under study are collected based on predetermined uniform measurement point locations to obtain a sparse measurement point test dataset; POD decomposition is performed on the CFD dataset to obtain POD modes and modal coefficients; the modal coefficients are corrected using the Gappy POD algorithm based on the sparse measurement point test dataset to obtain corrected modal coefficients; the Gappy POD reconstructed flow field parameters are obtained based on the POD modes and the corrected modal coefficients; using the measurement point locations as design variables, the global low-fidelity flow field parameters and the Gappy POD algorithm are used to construct the reconstructed flow field parameters. The design variables are optimized with the goal of minimizing the error between the POD reconstructed flow field parameters to obtain the optimal measurement point location. Based on the optimal measurement point location, test data under wind tunnel testing of the turbine blade under study are collected to obtain high-fidelity discrete test data. Based on the high-fidelity discrete test data, the modal coefficients are corrected using the Gappy POD algorithm to obtain optimized modal coefficients. Based on the POD modes and the optimized modal coefficients, the flow field reconstruction results of the turbine blade under study are obtained.

[0061] Alternatively, when the processor executes the computer program, it implements the functions of each module in the flow field reconstruction system based on experimental measurement point location optimization and multi-source data fusion, for example: The system comprises the following modules: a data sampling module for sampling the design space variables of the turbine blade under study to obtain sampled data; a dataset establishment module for performing CFD simulation calculations on the sampled data to obtain global low-fidelity flow field parameters; establishing a CFD dataset based on the global low-fidelity flow field parameters; collecting test data from wind tunnel tests of the turbine blade under study based on predetermined uniform measurement point locations to obtain a sparse measurement point test dataset; a POD flow field feature extraction module for performing POD decomposition on the CFD dataset to obtain POD modes and modal coefficients; an initial sampling coefficient correction module for correcting the modal coefficients using the Gappy POD algorithm based on the sparse measurement point test dataset to obtain corrected modal coefficients; an initial sampling flow field reconstruction module for obtaining Gappy POD reconstructed flow field parameters based on the POD modes and the corrected modal coefficients; and a measurement point optimization module for optimizing the flow field using the measurement point location as a design variable and the global low-fidelity flow field parameters and the Gappy POD algorithm. The design variables are optimized to minimize the error between the POD reconstructed flow field parameters to obtain the optimal measurement point location. The experimental module collects test data from wind tunnel tests of the turbine blade under study based on the optimal measurement point location, obtaining high-fidelity discrete test data. The optimized sampling coefficient correction module corrects the modal coefficients using the Gappy POD algorithm based on the high-fidelity discrete test data, obtaining optimized modal coefficients. The optimized sampling flow field reconstruction module obtains the flow field reconstruction results of the turbine blade under study based on the POD modes and the optimized modal coefficients.

[0062] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, wherein the instruction segments describe the execution process of the computer program in the flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion.

[0063] For example, the computer program can be divided into a data sampling module, a dataset establishment module, a POD flow field feature extraction module, an initial sampling coefficient correction module, an initial sampling flow field reconstruction module, a measurement point optimization module, an experiment module, an optimized sampling coefficient correction module, and an optimized sampling flow field reconstruction module. The specific functions of each module are as follows: The data sampling module is used to sample the design space variables of the turbine blade under study to obtain sampled data samples; the dataset establishment module is used to perform CFD simulation calculations on the sampled data samples to obtain global low-fidelity flow field parameters; based on the global low-fidelity flow field parameters, a CFD dataset is established; based on predetermined uniform measurement point locations, experimental data under wind tunnel testing of the turbine blade under study is collected to obtain a sparse measurement point experimental dataset; the POD flow field feature extraction module is used to perform POD decomposition on the CFD dataset to obtain POD modes and modal coefficients; the initial sampling coefficient correction module is used to, based on the sparse measurement point experimental dataset, utilize Gappy... The POD algorithm corrects the modal coefficients to obtain corrected modal coefficients; the initial sampling flow field reconstruction module is used to obtain Gappy POD reconstructed flow field parameters based on the POD modes and the corrected modal coefficients; the measurement point optimization module is used to optimize the design variables with the measurement point location as the design variable and the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters to obtain the optimal measurement point location; the experimental module is used to collect experimental data under wind tunnel testing of the turbine blade under study based on the optimal measurement point location to obtain high-fidelity discrete experimental data; the optimized sampling coefficient correction module is used to correct the modal coefficients using the Gappy POD algorithm based on the high-fidelity discrete experimental data to obtain optimized modal coefficients; the optimized sampling flow field reconstruction module is used to obtain the flow field reconstruction results of the turbine blade under study based on the POD modes and the optimized modal coefficients.

[0064] The flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above examples of flow field reconstruction devices based on experimental measurement point location optimization and multi-source data fusion do not constitute a limitation on such devices. The device may include more components than described above, or combine certain components, or use different components. For example, the flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion may also include input / output devices, network access devices, buses, etc.

[0065] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. This processor is the control center of the flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion, connecting all parts of the device via various interfaces and lines.

[0066] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the flow field reconstruction device based on test measurement point location optimization and multi-source data fusion by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0067] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0068] Example 4 This embodiment 4 also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion, for example: The design space variables of the turbine blade under study are sampled to obtain sampled data samples; CFD simulation calculations are performed on the sampled data samples to obtain global low-fidelity flow field parameters; a CFD dataset is established based on the global low-fidelity flow field parameters; test data under wind tunnel tests of the turbine blade under study are collected based on predetermined uniform measurement point locations to obtain a sparse measurement point test dataset; POD decomposition is performed on the CFD dataset to obtain POD modes and modal coefficients; the modal coefficients are corrected using the Gappy POD algorithm based on the sparse measurement point test dataset to obtain corrected modal coefficients; the Gappy POD reconstructed flow field parameters are obtained based on the POD modes and the corrected modal coefficients; using the measurement point locations as design variables, the global low-fidelity flow field parameters and the Gappy POD algorithm are used to construct the reconstructed flow field parameters. The design variables are optimized with the goal of minimizing the error between the POD reconstructed flow field parameters to obtain the optimal measurement point location. Based on the optimal measurement point location, test data under wind tunnel testing of the turbine blade under study are collected to obtain high-fidelity discrete test data. Based on the high-fidelity discrete test data, the modal coefficients are corrected using the Gappy POD algorithm to obtain optimized modal coefficients. Based on the POD modes and the optimized modal coefficients, the flow field reconstruction results of the turbine blade under study are obtained.

[0069] If the modules / units of the flow field reconstruction system based on test measurement point location optimization and multi-source data fusion are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0070] Based on this understanding, the present invention can implement all or part of the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0071] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0072] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0073] Example 5 This embodiment 5 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the flow field reconstruction device based on test measurement point location optimization and multi-source data fusion reads the computer program from the computer-readable storage medium and executes the computer program, so that the flow field reconstruction device based on test measurement point location optimization and multi-source data fusion can execute the flow field reconstruction method based on test measurement point location optimization and multi-source data fusion described in embodiment 1, which will not be repeated here.

[0074] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0075] The flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion described in this invention, after determining the design spatial variables and their range of variation, performs sample collection to establish initial sample data; it uses numerical computation CFD methods to obtain a global flow field distribution dataset of low-fidelity samples and sparsely distributes the experimental measurement point dataset; it uses an intrinsic orthogonal decomposition (POD) reduced-order model to extract key modal features of the CFD global flow field, i.e., the orthogonal vector basis corresponding to the high-dimensional vector group, and uses the Gappy POD method to obtain the corrected modal coefficients from the sparse experimental measurement point dataset, thereby obtaining a high-fidelity global flow field distribution; based on this, the location of the experimental measurement points is used as the optimization variable, and the Gappy POD method is used to obtain the corrected modal coefficients from the sparse experimental measurement point dataset. The objective function is to minimize the flow field reconstruction error obtained by POD. Sparse experimental measurement point locations are optimized to obtain the optimal measurement point locations. Based on the optimal measurement point locations, optimal sparse experimental measurement point data in the high-dimensional flow field are collected, and a high-precision global flow field distribution is further obtained. This method effectively utilizes the strong correlation between experimental and CFD multi-source data, combining the global nature of CFD data with the discrete high-fidelity characteristics of experimental measurement point data. The Gappy POD method reconstructs a high-precision, refined global flow field distribution, effectively solving the problems of mismatched spatial distribution characteristics and different fidelity between experimental and CFD data.

[0076] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion, characterized in that, include: The design space variables of the turbine blade under study are sampled to obtain sampled data. CFD simulation calculations were performed on the sampled data to obtain global low-fidelity flow field parameters; Based on the global low-fidelity flow field parameters, a CFD dataset is established; based on the predetermined uniform measurement point locations, test data under the wind tunnel test of the turbine blade to be studied are collected to obtain a sparse measurement point test dataset. The CFD dataset is subjected to POD decomposition to obtain POD modes and mode coefficients; Based on the sparse measurement point test dataset, the modal coefficients are corrected using the Gappy POD algorithm to obtain the corrected modal coefficients; Based on the POD mode and the correction mode coefficients, the Gappy POD reconstructed flow field parameters are obtained; Using the measurement point location as the design variable, and with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, the design variable is optimized to obtain the optimal measurement point location. Based on the optimal measurement point location, test data under the wind tunnel test of the turbine blade to be studied are collected to obtain high-fidelity discrete test data; Based on the high-fidelity discrete experimental data, the modal coefficients are corrected using the Gappy POD algorithm to obtain optimized modal coefficients; Based on the POD mode and the optimized mode coefficients, the flow field reconstruction results of the turbine blade under study are obtained.

2. The flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion according to claim 1, characterized in that, The design space variables of the turbine blade to be studied include the exit isentropic Mach number, inlet angle of attack, inlet turbulence intensity, and relative axial pitch.

3. The flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion according to claim 1, characterized in that, The process of collecting test data from wind tunnel tests on turbine blades under study, based on predetermined uniform measurement point locations, to obtain a sparse measurement point test dataset includes: Determine the wind tunnel test conditions for the turbine blade under study under initial sampling; wherein, the wind tunnel test conditions for the turbine blade under study under initial sampling include the pre-determined uniform measurement point locations; Based on the wind tunnel test conditions of the turbine blade under initial sampling, test data under the wind tunnel test of the turbine blade under study were collected to obtain a sparse measurement point test dataset.

4. The flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion according to claim 1, characterized in that, Based on the sparse measurement point test dataset and combined with the POD modes, the process of obtaining the corrected mode coefficients using the Gappy POD algorithm includes: Based on the sparse measurement point test dataset and the POD mode, a modal coefficient optimization formula is constructed; The modal coefficient optimization formula is solved using the least squares method to obtain the corrected modal coefficients.

5. The flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion according to claim 4, characterized in that, The modal coefficient optimization formula is as follows: in, To correct the modal coefficients; The data consists of discrete test measurement points; These are the modal coefficients; It is a POD mode; The modal order; For the first Test data at each measuring point; This represents the total number of measurement points.

6. The flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion according to claim 1, characterized in that, The process of optimizing the design variables, with the measurement point location as the design variable and minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters as the objective, to obtain the optimal measurement point location includes: Determine the number of measuring points; The initial values ​​of the measuring points are determined based on the number of measuring points; where the initial values ​​of the measuring points are several equally spaced measuring points on the turbine blade under study from the leading edge to the trailing edge. Based on the initial value of the measurement point location, with the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, and with the measurement point location as the design variable, an objective function is established. Based on a global optimization algorithm, the objective function is optimized to obtain the optimal measurement point location.

7. A flow field reconstruction system based on experimental measurement point location optimization and multi-source data fusion, characterized in that, include: The data sampling module is used to sample the design space variables of the turbine blade under study and obtain sampled data samples. The dataset establishment module is used to perform CFD simulation calculations on the sampled data to obtain global low-fidelity flow field parameters; based on the global low-fidelity flow field parameters, a CFD dataset is established; based on the predetermined uniform measurement point positions, test data under the wind tunnel test of the turbine blade to be studied is collected to obtain a sparse measurement point test dataset. The POD flow field feature extraction module is used to perform POD decomposition on the CFD dataset to obtain POD modes and mode coefficients. The initial sampling coefficient correction module is used to correct the modal coefficients based on the sparse measurement point test dataset using the Gappy POD algorithm to obtain the corrected modal coefficients. The initial sampling flow field reconstruction module is used to obtain the Gappy POD reconstructed flow field parameters based on the POD mode and the correction mode coefficients; The measurement point optimization module is used to optimize the design variables with the measurement point location as the design variable and the goal of minimizing the error between the global low-fidelity flow field parameters and the Gappy POD reconstructed flow field parameters, so as to obtain the optimal measurement point location. The test module is used to collect test data under wind tunnel testing of the turbine blade under study based on the optimal measurement point position, and obtain high-fidelity discrete test data. An optimized sampling coefficient correction module is used to correct the modal coefficients based on the high-fidelity discrete experimental data using the Gappy POD algorithm to obtain optimized modal coefficients. The optimized sampling flow field reconstruction module is used to obtain the flow field reconstruction results of the turbine blade under study based on the POD mode and the optimized mode coefficients.

8. A flow field reconstruction device based on experimental measurement point location optimization and multi-source data fusion, characterized in that, include: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, performs the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the flow field reconstruction method based on the optimization of test measurement point positions and the fusion of multi-source data as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the flow field reconstruction method based on experimental measurement point location optimization and multi-source data fusion as described in any one of claims 1-6.

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