Contraction section structure information determination method of water tunnel equipment and related product

Abstract

The invention discloses a method for determining structure information of a contraction section of water tunnel equipment and a related product, and is applied to the technical field of information processing. The method comprises the following steps: acquiring at least two candidate structural feature groups of a type of pipelines; according to the at least two candidate structural feature groups, determining flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups; according to the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups, screening M types of target structural features from the N types of candidate structural features; and according to the M types of target structure features, determining water tunnel pipeline information of one type of pipelines. According to the method, the water tunnel pipeline information of one type of pipelines can be determined based on the accurately screened multiple types of target structure features, the performance optimization of the water tunnel equipment can be more efficiently and comprehensively realized, and the research process of the hydrodynamic characteristics of the underwater vehicle is accelerated.

Description

Technical Field

[0001] The present application belongs to the field of information processing technology, and in particular relates to a method for determining structural information of a contraction section of a water tunnel device and related products. Background Art

[0002] A water tunnel is a device used to study the hydrodynamic characteristics of underwater vehicles and can be applied to their design. It consists of a type of pipe and a test pipe associated with the pipe. The type of pipe includes a straightening section, a contraction section, a diffusion section, and a pressure regulating section. The test pipe includes a test section. The structure of the type of pipe affects the flow field quality of the test pipe, and the quality of the flow field affects the accuracy of the results of underwater vehicle hydrodynamic research.

[0003] In related technologies, single-factor design methods can be used to determine the structural characteristics of a type of pipe. The structural characteristics of the contraction section include, but are not limited to, the contraction ratio, curve type, and pipe length. For example, single-factor design methods can determine the structural characteristics of a contraction pipe by controlling the contraction ratio while maintaining the curve type and pipe length constant. However, this method is inefficient in determining pipe information for water tunnel equipment, hindering the progress of research on the hydrodynamic characteristics of underwater vehicles. Summary of the Invention

[0004] Embodiments of the present application provide a method for determining the structural information of the contraction section of a water tunnel device and related products. Specifically, the related products may include an apparatus, device, medium, and program product for determining the structural information of the contraction section of a water tunnel device, which can solve the problem of low efficiency in determining pipeline information of a water tunnel device using a single-factor design method.

[0005] In a first aspect, an embodiment of the present application provides a method for determining structural information of a contraction section of a water tunnel device, wherein the water tunnel device includes a type of pipe and a test pipe associated with the type of pipe, wherein the structure of the type of pipe affects the flow field quality of the test pipe, the type of pipe is a contraction section, and the test pipe is a test section; the method for determining structural information of the contraction section of the water tunnel device includes: Obtain at least two candidate structural feature groups for a type of pipeline, each candidate structural feature group including N types of candidate structural features and feature quantities corresponding to each type of candidate structural features, where N is greater than or equal to 3; Determining flow field quality response information corresponding to each of the at least two candidate structural feature groups based on the at least two candidate structural feature groups; wherein the flow field quality response information is used to reflect the quality of the flow field of the test pipeline affected by a type of pipeline constructed by the candidate structural feature groups; screening M types of target structural features from N types of candidate structural features according to flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups, where M is greater than or equal to 2; According to the M types of target structural features, water tunnel pipeline information of a type of pipeline is determined, and the water tunnel pipeline information includes information used to characterize the structure of a type of pipeline.

[0006] In some possible implementations of the embodiments of the present application, determining, based on at least two candidate structural feature groups, flow field quality response information corresponding to each of the at least two candidate structural feature groups includes: Constructing a virtual water tunnel device model corresponding to each candidate structural feature group in at least two candidate structural feature groups, wherein the virtual water tunnel device model includes a virtual pipe model and a virtual test pipe model; Through the virtual fluid environment, a steady-state simulation is performed on the virtual water tunnel equipment model to obtain the flow field information of the virtual position points of the virtual test pipeline model. The flow field information is used to represent the flow state of the fluid flowing through the virtual test pipeline model at the virtual position points. The flow field quality response information is determined based on the flow field information of the virtual position points through the flow field quality detection algorithm corresponding to the virtual test pipeline model.

[0007] In some possible implementations of the embodiments of the present application, the flow field quality response information includes turbulence and velocity non-uniformity, and the flow field quality detection algorithm includes a turbulence detection algorithm and a velocity non-uniformity detection algorithm; The flow field quality detection algorithm corresponding to the virtual test pipeline model is used to determine the flow field quality response information based on the flow field information at the virtual position point, including: The turbulence detection algorithm corresponding to the virtual test pipeline model is used to determine the turbulence based on the flow field information at the virtual position point. The turbulence is used to characterize the pulsation intensity of the fluid velocity of the virtual test pipeline model in the virtual fluid environment. The velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model is used to determine the velocity non-uniformity based on the flow field information of the virtual position points. The velocity non-uniformity is used to characterize the degree of non-uniformity in the spatial distribution of the fluid velocity of the virtual test pipeline model in the virtual fluid environment.

[0008] In some possible implementations of the embodiments of the present application, screening M types of target structural features from N types of candidate structural features based on flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups includes: Determining a sensitivity value corresponding to each candidate structural feature based on flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups; the sensitivity value is used to characterize the degree of influence of the candidate structural feature on the flow field quality of the test pipeline; According to the sensitivity value corresponding to each candidate structural feature, M types of target structural features are screened from N types of candidate structural features, and the sensitivity value of the target structural feature is greater than or equal to the preset sensitivity value.

[0009] In some possible implementations of the embodiments of the present application, determining a sensitivity value corresponding to each candidate structural feature according to flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups includes: determining, based on the flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups, a relationship between N types of candidate structural features and the flow field quality response information; According to the relationship between N types of candidate structural features and flow field quality response information, the sensitivity value corresponding to each candidate structural feature is determined.

[0010] In some possible implementations of the embodiments of the present application, the water tunnel pipeline information includes a target feature quantity corresponding to each type of target structural feature in M types of target structural features; and determining the water tunnel pipeline information of a type of pipeline based on the M types of target structural features includes: Determine P first reference feature quantities corresponding to each type of target structure feature according to the feature quantity corresponding to each type of target structure feature in the M types of target structure features; Randomly combine the P first reference feature quantities corresponding to each type of target structural feature in the M types of target structural features to obtain L first test structural feature groups; According to the flow field quality information corresponding to each first test structure feature group in the L first test structure feature groups, Q second reference feature quantities are selected from the P first reference feature quantities corresponding to each type of target structure feature; Through the orthogonal experimental design method, the Q second reference feature quantities corresponding to each type of target structural feature in the M types of target structural features are combined to generate L second experimental structural feature groups; screening a target structure feature group from the L second test structure feature groups according to flow field quality response information corresponding to each second test structure feature group in the L second test structure feature groups; The water tunnel pipeline information of a type of pipeline is determined according to the target structural feature group.

[0011] In some possible implementations of the embodiments of the present application, the water tunnel pipeline information includes a target feature quantity corresponding to each type of target structure feature in the M types of target structure features; Before determining the water tunnel pipeline information of a type of pipeline according to the target structural feature group, the method for determining the contraction section structural information of the water tunnel device further includes: Obtaining measured flow field quality response information of a water tunnel equipment physical model, which is constructed based on a target structural feature set; According to the target structural feature group, the water tunnel pipeline information of a type of pipeline is determined, including: When the difference between the measured flow field quality response information and the flow field quality response information corresponding to the target structure feature group is less than or equal to the preset difference, the second reference feature quantity corresponding to each type of target structure feature in the target structure feature group is determined as the target feature quantity corresponding to each type of target structure feature.

[0012] In a second aspect, an embodiment of the present application provides a device for determining structural information of a contraction section of a water tunnel device, wherein the water tunnel device includes a type of pipe and a test pipe associated with the type of pipe, wherein the structure of the type of pipe affects the flow field quality of the test pipe, the type of pipe is a contraction section, and the test pipe is a test section; the device for determining structural information of the contraction section of the water tunnel device includes: A first acquisition module is configured to acquire at least two candidate structural feature groups for a type of pipe, each candidate structural feature group including N types of candidate structural features and feature quantities corresponding to each type of candidate structural features, where N is greater than or equal to 3; A first determining module is configured to determine, based on at least two candidate structural feature groups, flow field quality response information corresponding to each of the at least two candidate structural feature groups; wherein the flow field quality response information is configured to reflect the quality of the flow field of a test pipeline affected by a type of pipeline constructed by the candidate structural feature groups; a screening module, configured to screen M types of target structural features from N types of candidate structural features according to flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups, where M is greater than or equal to 2; The second determination module is used to determine the water tunnel pipeline information of a type of pipeline according to the M types of target structure characteristics, where the water tunnel pipeline information includes information used to characterize the structure of the type of pipeline.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for determining the contraction section structural information of a water tunnel device as described in any one of the first aspects is implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a method for determining the contraction section structural information of a water tunnel device as described in any one of the first aspects is implemented.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, it implements the method for determining the contraction section structural information of a water tunnel device as described in any one of the first aspects.

[0016] The method for determining the structural information of the contraction section of the water tunnel equipment of the embodiment of the present application and the related products can reflect the influence of different structural feature groups on the flow field quality of the test pipeline by determining the flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups. Subsequently, based on the comprehensive evaluation of the flow field quality response information of multiple groups of candidate structural feature groups, M types of target structural features are screened from N types of candidate structural features, and multiple types of structural features that have a greater impact on the flow field quality of the water tunnel equipment can be accurately screened out. Compared with the single-factor design method of checking one by one, this method takes into account the synergistic effect of multiple structural features, can quickly determine the main structural features that affect the flow field quality of the test pipeline, avoid multiple invalid tests on secondary structural features, and effectively improve the efficiency of determining water tunnel pipeline information. Determining the water tunnel pipeline information of a type of pipeline based on the accurately screened multiple types of target structural features can more efficiently and comprehensively achieve the performance optimization of the water tunnel equipment, thereby accelerating the research process of the hydrodynamic characteristics of underwater vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 Schematic diagram showing the structure of the water tunnel equipment provided by some embodiments of the present application Figure 2 A schematic flow chart of a method for determining structural information of a contraction section of a water tunnel device provided in some embodiments of the present application is shown; Figure 3 A flowchart illustrating a specific implementation of step 220 provided in some embodiments of the present application is shown; Figure 4 A flowchart illustrating a specific implementation of step 2203 provided in some embodiments of the present application is shown; Figure 5 A flowchart illustrating a specific implementation of step 230 provided in some embodiments of the present application is shown; Figure 6 A flowchart illustrating a specific implementation of step 2301 provided in some embodiments of the present application is shown; Figure 7 A flowchart illustrating a specific implementation of step 240 provided in some embodiments of the present application is shown; Figure 8 A schematic structural diagram of a contraction section structure corresponding to a second experimental structural feature group provided in some embodiments of the present application is shown; Figure 9 A diagram showing a change trend of the turbulent section of a water tunnel pipe corresponding to the second test structure feature group provided in some embodiments of the present application is shown; Figure 10 A relationship diagram for characterizing a second reference characteristic quantity and turbulence provided in some embodiments of the present application is shown; Figure 11 A relationship diagram for characterizing a second reference characteristic quantity and velocity non-uniformity provided in some embodiments of the present application is shown; Figure 12 A schematic structural diagram showing a contraction segment structure corresponding to a target feature group provided in some embodiments of the present application is shown; Figure 13 A schematic diagram illustrating the structure of a device for determining structural information of a contraction section of a water tunnel device provided in some embodiments of the present application is shown; Figure 14 A schematic structural diagram of an electronic device provided in some embodiments of the present application is shown. DETAILED DESCRIPTION

[0019] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0020] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0021] It should be noted that the acquisition, storage, use and processing of data in the embodiments of this application are in compliance with the relevant provisions of national laws and regulations.

[0022] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0023] Before describing the technical solutions provided by the embodiments of the present application, in order to facilitate understanding of the embodiments of the present application, the present application first specifically describes the relevant technologies involved: like Figure 1 As shown, the water tunnel apparatus includes a test section 101, a rectifying section 102, a contracting section 103, a diffuser section 104, and a pressure regulating section 105. These three sections are connected in sequence. Water flows into the rectifying section 102 from its inlet, passes through the contracting section 103, the test section 101, and the diffuser section 104, before exiting the pressure regulating section 105 from its outlet. These various pipe sections operate in concert to influence the flow quality within the test section 101.

[0024] The rectifying section 102 serves as a water flow pre-treatment unit and has a dual structure of a honeycomb and a damping net. The honeycomb divides large-scale vortices into small-scale vortices, reducing the initial intensity of turbulence; the damping net accelerates energy dissipation by hindering water flow, further weakening turbulent pulsation. The contraction section 103 serves as a water flow control component. Its contraction ratio, contraction section curve shape, and length parameters affect the turbulence suppression effect. A larger contraction ratio can reduce turbulence; a reasonable contraction section curve design can reduce boundary layer separation and avoid vortex generation; the contraction section length affects the smoothness of the fluid acceleration process. The diffusion section 104 gradually converts the kinetic energy of high-speed water flow into pressure energy by expanding the cross-sectional area of the flow channel, avoiding pressure fluctuations and turbulence re-ignition caused by sudden changes in flow velocity downstream. The pressure regulating section 105 maintains the pressure balance of the entire water tunnel equipment by adjusting the outlet pressure.

[0025] The performance optimization of water tunnel equipment relies on the rational design of a type of pipe, which directly determines the flow field quality of the test section 101. The quality of this flow field, in turn, directly impacts the accuracy of the results of underwater vehicle hydrodynamic characteristics research. In related art, the structural characteristics of a type of pipe can be determined using a single-factor design method. The structural characteristics of the contraction section 103 may include, but are not limited to, contraction ratio, curve type, and pipe length. For example, the single-factor design method can determine the structural characteristics of the contraction pipe by controlling the contraction ratio while maintaining the curve type and pipe length constant. Because the single-factor design method can only explore the impact of a single variable on flow field quality at a time, to fully understand the impact of the interaction of multiple factors, such as contraction ratio, curve type, and pipe length, on the flow field quality of the test pipe, each change in a variable requires redesigning and rerunning the experiment, which consumes a significant amount of time and resources. This makes determining water tunnel equipment pipe information based on this method extremely inefficient, significantly slowing the progress of underwater vehicle hydrodynamic characteristics research.

[0026] In order to solve the above problems in the related art, the present invention provides a method for determining the structural information of the contraction section of a water tunnel device and related products. Figure 2 To the attached Figure 7 , the method for determining the contraction section structural information of the water tunnel equipment provided in the embodiment of the present application is described in detail through specific embodiments and application scenarios.

[0027] Figure 1 The flowchart of the method for determining the structural information of the contraction section of the water tunnel device provided by some embodiments of the present application is shown. The water tunnel device may include a type of pipe and a test pipe associated with the type of pipe. The structure of the type of pipe affects the flow field quality of the test pipe. The type of pipe is at least one pipe section among the rectifying section, the contraction section, the diffusion section and the pressure regulating section. The test pipe is the test section. The embodiment of the present application is exemplified by taking the type of pipe as the contraction section and the test pipe as the test section. Figure 1 As shown, the method for determining the structural information of the contraction section of the water tunnel device may include steps 210 to 240.

[0028] Step 210: Obtain at least two candidate structural feature groups for a type of pipe, each candidate structural feature group including N types of candidate structural features and feature quantities corresponding to each type of candidate structural features, where N is greater than or equal to 3; Step 220: Determine flow field quality response information corresponding to each of the at least two candidate structural feature groups based on the at least two candidate structural feature groups; wherein the flow field quality response information is used to reflect the quality of the flow field of the test pipeline affected by a type of pipeline constructed by the candidate structural feature group; Step 230 , screening M types of target structural features from N types of candidate structural features according to the flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups, where M is greater than or equal to 2; Step 240 : Determine water tunnel pipeline information of a type of pipeline based on the M types of target structural features. The water tunnel pipeline information includes information used to characterize the structure of a type of pipeline.

[0029] Therefore, by determining the flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups, the influence of different structural feature groups on the flow field quality of the test pipeline can be reflected. Subsequently, based on the comprehensive evaluation of the flow field quality response information of multiple groups of candidate structural feature groups, M types of target structural features are screened from N types of candidate structural features, which can accurately screen out multiple types of structural features that have a greater impact on the flow field quality of the water tunnel equipment. Compared with the single-factor design method of screening one by one, this method takes into account the synergistic effect of multiple structural features, can quickly determine the main structural features that affect the flow field quality of the test pipeline, avoid multiple invalid tests on secondary structural features, and effectively improve the efficiency of determining water tunnel pipeline information. Determining the water tunnel pipeline information of a type of pipeline based on accurately screened multiple types of target structural features can more efficiently and comprehensively achieve the performance optimization of water tunnel equipment, thereby accelerating the research process of the hydrodynamic characteristics of underwater vehicles.

[0030] The above steps are described in detail below.

[0031] First, before executing step 210, it is necessary to first determine at least two candidate structural feature groups in step 210. Based on this, the method for determining the contraction section structural information of the water tunnel device may further include: An initial structural feature set is obtained, which includes N categories of candidate structural features and reference feature quantities corresponding to each category of candidate structural features; at least two feature quantities corresponding to each category of candidate structural features are determined based on the reference feature quantities corresponding to each category of candidate structural features in the N categories of candidate structural features; at least two candidate structural feature groups are determined based on the N categories of candidate structural features and the at least two feature quantities corresponding to each category of candidate structural features.

[0032] Taking a pipe as an example, the N categories of candidate structural features include contraction length, contraction ratio, contraction curve type, inlet shape, outlet shape, and number of contraction surfaces. Reference features for contraction length may include, but are not limited to, 0.8 meters to 2.4 meters; reference features for contraction ratio may include, but are not limited to, 1:3 to 1:9; reference features for contraction curve type may include, but are not limited to, quintic, Vickers, and Batchelor-Shaw (BS) curves; reference features for inlet shape may include, but are not limited to, square and circular; reference features for outlet shape may include, but are not limited to, square and circular; and reference features for the number of contraction surfaces may include, but are not limited to, three and four. Furthermore, to more intuitively and accurately analyze the impact of different structural features on contraction performance, an initial three-dimensional model of the contraction can be constructed based on the aforementioned N categories of candidate structural features and the reference features corresponding to each category. 3D modeling transforms abstract structural parameters into visual geometric forms, facilitating rapid assessment of the spatial layout and morphological differences of the contraction under different feature combinations.

[0033] For example, at least two feature quantities corresponding to each category of candidate structural features can be determined by an experimenter based on the reference feature quantities corresponding to each category of the N categories of candidate structural features. After determining the reference feature quantities corresponding to each category of candidate structural features, at least two candidate structural feature groups can be generated through a Plackett-Burman design (PBD), with the following specific steps: For N types of candidate structural features, the N types of candidate structural features are used as experimental factors, and two levels of high (+1) and low (-1) are set for each experimental factor, that is, the reference feature quantity corresponding to each type of candidate structural feature. For details, please refer to Table 1 below.

[0034]

[0035] Table 1 Next, a fractional factorial experimental plan is generated using a Plackett-Burman design, with the number of experiments being N = k + 1 (k being the number of experimental factors). This results in a series of experimental combinations, each corresponding to a candidate structural feature group.

[0036] It is worth noting here that the level setting of the characteristic quantity of each type of candidate structure is not limited to the above-mentioned two categories of high and low. The experimenter can flexibly set the above levels according to actual needs and research accuracy. It can be set to three levels, four levels, etc., to more carefully capture the impact of structural feature changes on the flow field quality of the test pipeline. This application does not make specific limitations on this.

[0037] Next, in step 220, the flow field quality response information may include turbulence and velocity nonuniformity. Turbulence is used to characterize the pulsation intensity of the fluid velocity in the flow field of the test pipeline, and velocity nonuniformity is used to reflect the degree of nonuniformity in the spatial distribution of the fluid velocity in the flow field of the test pipeline.

[0038] Among them, the higher the turbulence, the more disordered the fluid motion in the test pipe is, and the lower the velocity nonuniformity, the more uniform the fluid velocity distribution in the test pipe is.

[0039] In some embodiments of the present application, in order to accurately determine the flow field quality response information corresponding to each candidate structural feature group, such as Figure 3 As shown, the above step 220 may specifically include steps 2201 to 2203.

[0040] Step 2201 : constructing a virtual water tunnel equipment model corresponding to each candidate structural feature group in at least two candidate structural feature groups. The virtual water tunnel equipment model includes a virtual pipe model and a virtual test pipe model.

[0041] For example, first, a first virtual pipe model is constructed. This can include, for each candidate structural feature group, constructing a virtual pipe model based on N candidate structural features in the candidate structural feature group and the corresponding feature quantities for each candidate structural feature. For example, based on the N candidate structural features, such as contraction length, contraction ratio, contraction curve type, inlet shape, outlet shape, and number of contraction surfaces, as well as the specific feature quantities corresponding to these N candidate structural features, the virtual pipe model is constructed using 3D modeling software. Secondly, a virtual test pipe model is obtained. The virtual test pipe model can be pre-constructed based on the standard dimensions and physical properties of an actual water tunnel test pipe. It is understood that the virtual test pipe model can have fixed parameter settings to ensure consistency and comparability of the test environment during the evaluation of different candidate structural feature groups. Then, the virtual pipe model is connected to the virtual test pipe model to form a virtual water tunnel device model. Specifically, the outlet of the virtual pipe model can be connected to the inlet of the virtual test pipe model, and fluid flows from the virtual pipe model into the virtual test pipe model. Therefore, the structural characteristics of the virtual pipe model directly affect the state of the fluid flowing into the virtual test pipe model.

[0042] It is understandable that changes in the structural characteristics of a type of virtual pipeline model will cause changes in physical quantities such as velocity, pressure, and turbulence intensity of the fluid flowing into the virtual test pipeline model, thereby affecting the flow field quality within the virtual test pipeline model.

[0043] In step 2202, a steady-state simulation is performed on the virtual water tunnel equipment model through a virtual fluid environment to obtain flow field information of virtual positions of the virtual test pipeline model. The flow field information is used to represent the flow state of the fluid flowing through the virtual test pipeline model at the virtual positions.

[0044] Among them, the virtual fluid environment can be specifically a preset boundary condition, which can specifically include the inlet conditions, outlet conditions and wall conditions designed for the virtual water tunnel equipment model. For example, the inlet condition can be set to a uniform flow of the inlet fluid, and the turbulence intensity is set to 10%, indicating the degree of pulsation of the fluid velocity at the inlet; the eddy viscosity ratio is set to 6, which is used to describe the turbulent viscosity characteristics of the fluid; the turbulence length scale is set to 7% of the inlet side length, which reflects the average size of the turbulent vortex. The outlet condition can be set to a pressure outlet with a pressure value of 0, simulating the state of the outlet being connected to the atmosphere to ensure that the fluid can flow out of the calculation area smoothly. The wall condition can adopt a no-slip boundary condition for all walls, that is, the fluid velocity at the wall is 0.

[0045] For example, the constructed virtual water tunnel device model is first imported into computational fluid dynamics (CFD) software. The environmental parameters of the virtual fluid environment are set within the CFD software to perform transient simulations of the virtual water tunnel device model. Next, the computational domain of the virtual water tunnel device model is discretized using the CFD software's meshing tools. Structured hexahedral elements are used to mesh the virtual water tunnel device model, with localized refinement performed in complex flow areas such as the contraction section and the test section entrance, as well as near the walls. Furthermore, 10-15 expansion layers are set near the walls to accurately analyze the flow characteristics within the boundary layer. By adjusting the mesh size and density, the number of cells is controlled between 12 million and 15 million, ensuring both accuracy and efficiency. Finally, in the turbulence simulation settings, the Large Eddy Simulation (LES) model is selected to analyze the turbulent flow. This large eddy simulation model can directly calculate the large-scale vortex motion and model the small-scale vortex through a sub-grid model. Compared with the traditional Reynolds-averaged Navier-Stokes method, it can more realistically restore the turbulent pulsation characteristics. During the solution process, the filtered LES equation corresponding to the LES model is numerically solved to ensure that the equation satisfies the laws of conservation of mass and momentum on the discrete grid. Finally, the flow field information of the virtual position points inside the virtual water tunnel equipment model is extracted through computational fluid dynamics software, which may include fluid velocity, pressure, turbulence intensity and other information. Furthermore, the collected flow field information is visualized. For example, the acceleration and deceleration areas of the fluid in the virtual water tunnel equipment model are intuitively displayed through the velocity distribution cloud map, and the resistance distribution during the fluid flow process is analyzed using the pressure distribution map.

[0046] Step 2203 : Determine flow field quality response information based on the flow field information of the virtual position point using a flow field quality detection algorithm corresponding to the virtual test pipeline model.

[0047] The virtual locations may be pre-selected representative locations within the virtual test pipeline model, such as locations at the center section of the virtual test pipeline model or locations at a section 1 / 3 of the distance from the inlet of the virtual test pipeline model. The flow field information at the virtual locations may specifically include the instantaneous velocity of the fluid at the aforementioned virtual locations and the average velocity obtained through statistical analysis.

[0048] In some embodiments of the present application, flow field quality response information includes turbulence and velocity nonuniformity. Turbulence is used to characterize the pulsation intensity of the fluid velocity of the virtual test pipe model in the virtual fluid environment, that is, the intensity of the random fluctuations of the fluid under the average flow state. The higher the pulsation intensity, the greater the disorder of the fluid motion. In actual water tunnel tests, excessive turbulence may affect the accuracy and stability of the test data. Velocity nonuniformity is used to characterize the unevenness of the spatial distribution of the fluid velocity of the virtual test pipe model in the virtual fluid environment. The higher the velocity nonuniformity, the greater the difference in velocity across the cross section, which may cause adverse phenomena such as eddy currents and separation in the fluid flow, affecting the test accuracy.

[0049] Therefore, steady-state simulation of the virtual water tunnel equipment model in a virtual fluid environment can simulate fluid flow under different working conditions in a computer environment. Compared with real experiments, virtual simulation can flexibly set boundary conditions and initial conditions, quickly repeat the test, and greatly improve the verification efficiency of the design scheme. In addition, the flow field quality detection algorithm corresponding to the virtual test pipeline model is used to process the flow field information of the virtual position points. The obtained flow field quality response information enables the impact of different candidate structural feature groups on the water tunnel flow field quality to be compared and evaluated through specific numerical values. In this way, based on the quantitative flow field quality information, the advantages and disadvantages of each candidate structural feature group can be accurately judged, which helps to improve the design efficiency of water tunnel pipeline information.

[0050] In some embodiments of the present application, in order to accurately and comprehensively determine the flow field quality response information, the flow field quality detection algorithm may specifically include a turbulence detection algorithm and a velocity non-uniformity detection algorithm. Figure 4 As shown, the above step 2203 may specifically include step 22031 and step 22032.

[0051] Step 22031, using the turbulence detection algorithm corresponding to the virtual test pipeline model, the turbulence is determined according to the flow field information of the virtual position point; the turbulence is used to characterize the pulsation intensity of the fluid velocity of the virtual test pipeline model in the virtual fluid environment.

[0052] The flow field information of the virtual position point may include velocity information of the virtual position point at different moments within a preset time window.

[0053] The turbulence detection algorithm can be expressed by the following formula (1): (1) in, represents the turbulence, u '、 v '、 w ' are the pulsating velocities of the fluid at the virtual position point in the three coordinate axis directions, The average speed.

[0054] It is worth noting here that the pulsating velocity refers to the difference between the instantaneous velocity and the average velocity of the fluid, reflecting the fluctuation of the velocity in the time dimension; the time-averaged velocity is the stable velocity value obtained by averaging the fluid velocity within a preset time window, and is the benchmark parameter for calculating turbulence.

[0055] For example, for each virtual position point, according to the instantaneous speed value of the virtual position point in the three coordinate axis directions within the preset time window 、 、 Then, the instantaneous velocity in each coordinate axis direction is averaged over time to obtain the average velocity in each coordinate axis direction. 、 、 Then, according to the formula Get the pulsation speed in each coordinate axis direction respectively u '、 v '、 w '. Finally, the turbulence intensity at each virtual position point is obtained by the above formula (1).

[0056] In this way, the turbulence detection algorithm can convert the raw velocity data obtained from the simulation into a quantitative turbulence index, which intuitively reflects the pulsating characteristics of the fluid at different virtual locations and provides a basis for evaluating the turbulence suppression effect of candidate structural feature groups. For example, if the turbulence corresponding to a candidate structural feature group is low, it means that the design of the candidate structural feature group can help reduce the disordered fluctuations of the fluid in the water tunnel device, improve the flow field stability of the water tunnel device, and optimize the flow field quality of the test pipeline.

[0057] In step 22032, the velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model is used to determine the velocity non-uniformity based on the flow field information of the virtual position point. The velocity non-uniformity is used to characterize the degree of non-uniformity in the spatial distribution of the fluid velocity of the virtual test pipeline model in the virtual fluid environment.

[0058] The flow field information of the virtual position point may further include velocity information of the virtual position point within a preset cross section of the virtual test pipe model. The velocity information of the virtual position point within the preset cross section may include velocity information of the virtual position point at the center cross section of the virtual test pipe model and velocity information of the virtual position point at a cross section 1 / 3 from the inlet of the virtual test pipe model.

[0059] The velocity unevenness detection algorithm can be expressed by the following formula (2): (2) in, Indicates velocity unevenness, U indicates the average velocity of the preset cross section, and n indicates the number of virtual position points within the preset cross section. u i Represents the time-averaged velocity of the i-th virtual position point within the preset cross section.

[0060] For example, first, the average speed of each virtual position point in the preset cross section is extracted. u i ; Secondly, according to the average speed of n virtual position points u i Determine the average velocity U of the preset cross section; finally, use the above formula (2) to obtain the velocity non-uniformity of the preset cross section, and determine the velocity non-uniformity value of the preset cross section as the above velocity non-uniformity.

[0061] By calculating velocity nonuniformity using the velocity nonuniformity detection algorithm, we can quantitatively assess the impact of different candidate structural feature groups on the uniformity of velocity distribution within the test pipe. For example, if the velocity nonuniformity corresponding to a candidate structural feature group is small, it indicates that the pipe designed based on this candidate structural feature group can effectively promote uniform fluid flow in the water tunnel device, reduce local velocity anomalies, and optimize the flow field quality of the test pipe.

[0062] Therefore, by calculating turbulence and velocity nonuniformity, the fluid flow state within the virtual test pipe model can be quantified. Turbulence intuitively reflects the pulsation intensity of the fluid velocity, while velocity nonuniformity clearly reflects the degree of discreteness in the spatial distribution of the fluid velocity. Turbulence and velocity nonuniformity provide a clear and objective basis for evaluating the impact of different candidate structural feature groups on the flow field quality of the test pipe, avoiding the uncertainty of subjective judgment. Furthermore, based on the calculated turbulence and velocity nonuniformity, different candidate structural feature groups can be compared and analyzed. If the turbulence and velocity nonuniformity corresponding to a candidate structural feature group are both low, it indicates that this structural feature group is conducive to improving flow field quality and can serve as a reference for the subsequent optimization design of water tunnel equipment. In this way, by continuously adjusting the characteristic quantities of the candidate structural features, repeating simulation calculations and flow field quality assessments, the optimal design solution can be gradually found, thereby improving the accuracy and reliability of water tunnel testing.

[0063] Furthermore, with respect to step 230 , the target structural feature may be M types of candidate structural features having a critical impact on the flow field quality of the test pipeline obtained through sensitivity value screening.

[0064] In some embodiments of the present application, in order to accurately screen M types of target structural features from N types of candidate structural features, such as Figure 5 As shown, the above step 230 may specifically include step 2301 and step 2302.

[0065] Step 2301: Determine the sensitivity value corresponding to each candidate structural feature based on the flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups; the sensitivity value is used to represent the degree of influence of the candidate structural feature on the flow field quality of the test pipeline.

[0066] The sensitivity value is a numerical indicator used to quantify the impact of each candidate structural feature on the flow field quality of the test pipe. A larger sensitivity value indicates that the candidate structural feature has a more significant impact on the flow field quality (such as turbulence and velocity nonuniformity) of the test pipe when its characteristic quantity is changed, indicating that the structural feature is more important in the optimization design.

[0067] Step 2302 : Based on the sensitivity value corresponding to each candidate structural feature, M types of target structural features are screened from N types of candidate structural features, and the sensitivity value of the target structural feature is greater than or equal to a preset sensitivity value.

[0068] The preset sensitivity value is a pre-set threshold value used as a judgment criterion. When the sensitivity value of a candidate structural feature is greater than or equal to the preset sensitivity value, it indicates that the candidate structural feature has a significant impact on the flow field quality of the test pipeline and is selected as the target structural feature.

[0069] Exemplarily, the sensitivity value of each candidate structural feature calculated in step 2301 is compared with the preset sensitivity value one by one, and M types of structural features with sensitivity values greater than or equal to the preset sensitivity value are screened out from N types of candidate structural features as target structural features.

[0070] Based on Table 1 above, the sensitivity of the two types of flow field qualities, turbulence and velocity nonuniformity, to the six candidate structural features (X1-X6) in Table 1 was calculated (represented by P value, the larger the P value, the higher the sensitivity). The results are shown in Table 2.

[0071]

[0072] Table 2 Table 2 shows that for turbulence, the sensitivity ranking is: X2 > X1 > X3 > X4 > X5 > X6; for velocity nonuniformity, the sensitivity ranking is: X3 > X1 > X2 > X6 > X5 > X4. A comprehensive analysis of the two flow quality response information for turbulence and velocity nonuniformity shows that candidate structural features X1, X2, and X3 exhibit high sensitivity to both flow quality response information, indicating that X1, X2, and X3 have a significant impact on the flow quality of the test section. Based on this, the contraction section length X1, contraction ratio X2, and contraction curve type X3 are identified as target structural features. Candidate structural features X4, X5, and X6 exhibit low sensitivity to both flow quality response information. Therefore, in the subsequent structural optimization process for the contraction section, these can be set to fixed values. For example, X4 is set to a square shape, X5 is set to a square shape, and X6 is set to a four-sided contraction shape.

[0073] Therefore, through quantitative evaluation and screening of sensitivity values, it is possible to accurately identify M target structural features that significantly impact the flow field quality of the test pipeline from N candidate structural features. After clarifying the target structural features, the characteristic quantities of these key features (i.e., target structural features) can be optimized and designed, reducing unnecessary tests and simulations, reducing the time cost and computing resource consumption during the test process, and thus improving the efficiency of determining pipeline information for water tunnel equipment.

[0074] In some embodiments of the present application, Figure 6 As shown, the above step 2301 may specifically include step 23011 and step 23012.

[0075] Step 23011: Determine the relationship between N types of candidate structural features and flow field quality response information based on the flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups.

[0076] For example, the candidate structural features in each candidate structural feature group (such as the length of the contraction section, the contraction ratio, the type of contraction curve, the inlet shape, the outlet shape, and the number of contraction surfaces) are used as independent variables, and the flow field quality response information (such as turbulence or velocity non-uniformity) is used as the dependent variable. By collecting data points of at least two candidate structural feature groups and their corresponding flow field quality response information, a polynomial function is fitted using methods such as the least squares method to obtain a response surface model. The response surface model can intuitively display the relationship between N types of candidate structural features and flow field quality response information. For example, based on the above method, a response surface model based on turbulence and a response surface model based on velocity non-uniformity can be constructed respectively.

[0077] Step 23012: Determine the sensitivity value corresponding to each candidate structural feature based on the relationship between the N types of candidate structural features and the flow field quality response information.

[0078] For example, after determining the relationship between N types of candidate structural features and flow field quality response information, sensitivity analysis of the candidate structural features can be performed by means of regression model, variance analysis, numerical simulation or experiment.

[0079] Taking the regression model as an example, the partial derivative of the response surface model with respect to each candidate structural feature can be calculated. The value of the partial derivative reflects the degree to which a small change in the candidate structural feature of that class affects the flow field quality response information, while the other candidate structural features remain unchanged. This is the sensitivity value. It can be understood that the larger the absolute value of the partial derivative, the greater the sensitivity value corresponding to the candidate structural feature.

[0080] Therefore, by calculating and screening the sensitivity values, the influence of each candidate structural feature on the flow field quality of the test pipeline can be quantified, so as to focus on those target structural features that have a greater impact on the flow field quality and avoid conducting too many tests on candidate structural features that have a smaller impact on the results, thereby more efficiently determining the water tunnel pipeline information of the water tunnel equipment.

[0081] Then, in step 240, the water tunnel pipeline information includes the target feature quantity corresponding to each type of target structure feature in the M types of target structure features. In some embodiments of the present application, such as Figure 7 As shown, the above step 240 may specifically include steps 2401 to 2406.

[0082] Step 2401 : Determine P first reference feature quantities corresponding to each type of target structure feature according to the feature quantity corresponding to each type of target structure feature in the M types of target structure features.

[0083] For example, P feature quantities can be selected from the feature quantities corresponding to each type of target structural feature by selecting them at equal intervals as the first reference feature quantities. For example, M types of target structural features may include the contraction segment length X1, the contraction ratio X2, and the contraction curve type X3. The feature quantities corresponding to each type of target structural feature can refer to Table 1 above, where the P first reference feature quantities corresponding to each type of target structural feature may include: X1 (1.4m, 1.6m, 1.8m, 2.0m, 2.2m, 2.4m, 2.6m, 2.8m), X2 (1:3, 1:5, 1:7, 1:9, 1:12), and X3 (quintic, Vickers, BS).

[0084] Furthermore, in some embodiments of the present application, before step 2401, a steepest climb experiment can be performed to determine the feature quantity corresponding to each type of target structural feature from the reference feature quantities corresponding to each type of target structural feature.

[0085] Specifically, the steepest ramp test involves determining the ramp direction using the response surface model determined in step 2301. The coefficients in the response surface model are the coefficients of the Plackett-Burman Design (PBD) regression equation. These PBD regression equation coefficients represent the direction and degree of influence of the corresponding candidate structural feature on the flow quality response information. A positive PBD regression equation coefficient indicates a positive correlation between the candidate structural feature and the flow quality response information. That is, as the characteristic value of the candidate structural feature increases, the value of the flow quality response information also increases, representing a positive effect parameter. A negative PBD regression equation coefficient indicates a negative correlation between the candidate structural feature and the flow quality response information. As the characteristic value of the candidate structural feature increases, the value of the flow quality response information decreases, representing a negative effect parameter. The absolute value of the PBD regression equation coefficient reflects the strength of the candidate structural feature's influence. A larger absolute value indicates a more significant impact of the candidate structural feature on the flow quality response information, and the corresponding step size in the steepest ramp test will also be larger. During the ramp test, the characteristic quantities of the candidate structural features are gradually adjusted according to the set step size. After each adjustment, the test is repeated to measure the turbulence and velocity unevenness, and the relative errors compared to the target turbulence and velocity unevenness are calculated. When the relative errors reach a minimum and begin to increase, the test is stopped, and the reference characteristic quantities of the candidate structural features are determined. Subsequently, the reference characteristic quantity with the smallest error corresponding to the target structural feature in the steepest ramp test can be used as the center point for subsequent optimization, thereby determining the characteristic quantities corresponding to each type of target structural feature.

[0086] For example, the aforementioned steepest climb test can be used to determine the significance and optimal intervals for M-type target structural features (such as contraction length, contraction ratio, and contraction curve type). Targeted, equally spaced selections can be made near the optimal intervals to obtain feature quantities corresponding to each type of target structural feature within the M-type target structural features. For example, if the steepest climb test determines that the contraction length is optimally within the range of 1.4-2.8m, then in step 2401, when selecting the first reference feature quantity for X1, selections can be made primarily within this range, such as 1.4m, 1.6m, 1.8m, 2.0m, and so on, to ensure that the selected feature quantities are more reasonable and representative.

[0087] Step 2402 : Randomly combine the P first reference feature quantities corresponding to each type of target structural feature in the M types of target structural features to obtain L first test structural feature groups.

[0088] Exemplarily, a full combination approach is used to combine the first reference feature quantities corresponding to M types of target structural features. Specifically, for each type of target structural feature, a first reference feature quantity is selected from the corresponding P first reference feature quantities, and then these first reference feature quantities are combined together to form a first test structural feature group.

[0089] Step 2403 : Based on the flow field quality information corresponding to each of the L first test structural feature groups, Q second reference feature quantities are selected from the P first reference feature quantities corresponding to each type of target structural feature.

[0090] Exemplarily, for each of the L first experimental structural feature groups, a corresponding virtual water tunnel device model is constructed, and a steady-state simulation is performed in a virtual fluid environment to obtain flow field quality information of each virtual water tunnel device model. For details, please refer to the above steps 2201 to 2203, which will not be repeated here.

[0091] Subsequently, by comparing the flow field quality information when different characteristic quantities are taken for the same target structural feature in different first test structural feature groups, Q first reference characteristic quantities with a better flow field quality improvement effect can be determined from the P first reference characteristic quantities corresponding to each type of target structural feature based on the preset flow field quality information, and these first reference characteristic quantities can be used as second reference characteristic quantities. For example, if the preset turbulence segment in the preset flow field quality information is set to 4.5%, and the preset unevenness in the preset flow field quality information is set to 4.5%, then it can be determined that the Q second reference characteristic quantities corresponding to each type of target structural feature can include X1 (1.4m, 2.0m, 2.8m), X2 (1:3, 1:5, 1:9), and X3 (quintic, Vickers, BS), respectively.

[0092] Step 2404 : By using an orthogonal experimental design method, the Q second reference feature quantities corresponding to each type of target structural feature in the M types of target structural features are combined to generate L second experimental structural feature groups.

[0093] For example, an orthogonal experimental design method is used to select an appropriate orthogonal table based on the M types of target structural features and the Q second reference characteristic quantities corresponding to each type of target structural feature. An orthogonal table is a pre-designed table that ensures a comprehensive examination of the combinations of various experimental factors (target structural features) and experimental levels (second reference characteristic quantities) with a relatively small number of experiments. Next, the M types of target structural features are assigned to the columns of the orthogonal table, with the Q second reference characteristic quantities of each type of target structural feature corresponding to the levels in the orthogonal table. Each row of the orthogonal table corresponds to a parameter combination, thereby generating L second experimental structural feature groups.

[0094] Taking the M-type target structure characteristics including the contraction length X1, contraction ratio X2 and contraction curve type X3 as an example, a three-factor three-level orthogonal test can be set up. For each factor, three levels are selected. According to the selection principle of the orthogonal table, L9 (3 4 ) is used as the orthogonal table of the orthogonal experiment. According to the three types of target structural features and the three levels of characteristic quantities corresponding to each type of target structural features, the orthogonal table of the orthogonal experiment can be obtained, as shown in Table 3 below.

[0095]

[0096] Table 3 Step 2405 : Filter a target structural feature group from the L second test structural feature groups according to the flow field quality response information corresponding to each of the L second test structural feature groups.

[0097] Next, the method of steps 2201 to 2203 is used to determine the turbulence intensity and velocity non-uniformity corresponding to each of the nine second test structure feature groups (a) to (i).

[0098] In one example, the target structural feature group may include a first target structural feature group screened based on turbulence and a second target structural feature group screened based on velocity non-uniformity. Based on this, the first target structural feature group can be screened from L second test structural feature groups according to turbulence, and the second target structural feature group can be screened from L second test structural feature groups based on velocity non-uniformity.

[0099] Next, step 2405 will be described in detail with reference to the orthogonal table of the orthogonal experiment shown in Table 3 above.

[0100] First, based on the orthogonal table shown in Table 3 above, nine second experimental structure feature groups are constructed, and their corresponding contraction segment structures are as follows: Figure 8 As shown in subfigures (a) to (i).

[0101] In order to further analyze the influence of the contraction section structure constructed by the nine second test structure feature groups on the flow field quality of the test section affected by the contraction section structure, the turbulence of the central axis of the water tunnel pipe corresponding to each contraction section structure is calculated through the aforementioned step 220, and the following is constructed: Figure 9 The turbulent section change trend diagram is shown in the figure. Figure 9 Serial number (1) - serial number (9) and Figure 8 There is a one-to-one correspondence between subgraph (a) and subgraph (i).

[0102] See also Figure 9 It can be seen that the contraction section structure corresponding to serial number (8) shows the lowest turbulence of 6.4% at the test section. This contraction section structure corresponds to subgraph (h), and its corresponding second test structure feature group is: contraction ratio X2 is 1:9, contraction curve type X3 is Vickers, and contraction section length X1 is 1.4m.

[0103] Figure 8 The turbulence values at 1 / 3 of the flow direction of the test section affected by the structure of each contraction section are shown in Table (4).

[0104]

[0105] Table 4 Next, the nine second test structural feature groups are analyzed based on Table 4 above, so as to screen the first target structural feature group from the L second test structural feature groups based on turbulence: For the contraction ratio X2, the turbulence corresponding to all the second test structure feature groups (1, 2, 3) with a contraction ratio of 1:3 is added and recorded as K1 of X2; the turbulence corresponding to all the second test structure feature groups (4, 5, 6) with a contraction ratio of 1:5 is added and recorded as K2 of X2; the turbulence corresponding to all the second test structure feature groups (7, 8, 9) with a contraction ratio of 1:9 is added and recorded as K3 of X2. Then: K1 of X2 = 0.097 + 0.09 + 0.073 = 0.260; K2 of X2 = 0.094 + 0.094 + 0.093 = 0.281; K3 of X2 = 0.088 + 0.064 + 0.071 = 0.223; then, calculate the average value of K1 of X2 =K1 / 3=0.260 / 3=0.0867; the average value of K2 of X2 =K2 / 3=0.281 / 3=0.0937; the average value of K3 of X2 =K3 / 3=0.223 / 3=0.0743; then calculate the range R of X2: R of X2=0.937-0.0743=0.0194.

[0106] For the contraction curve type X3, the sum of the turbulence corresponding to all the second test structure characteristic groups (1, 4, 7) with the contraction curve type of quintic is recorded as K1 of X3; the sum of the turbulence corresponding to all the second test structure characteristic groups (2, 5, 8) with the contraction curve type of Vickers is recorded as K2 of X3; the sum of the turbulence corresponding to all the second test structure characteristic groups (3, 6, 9) with the contraction curve type of BS is recorded as K3 of X3, then: K1 of X3 = 0.097 + 0.094 + 0.088 = 0.279; K2 of X3 = 0.090 + 0.094 + 0.064 = 0.248; K3 of X3 = 0.073 + 0.093 + 0.071 = 0.237; then, calculate the average value of K1 of X3 =0.093; the average value of K2 of X3 =0.0827; the average value of K3 of X3 =0.079; then, calculate the range R of X3: R of X3=0.093-0.079=0.014.

[0107] For the contraction section length X1, the turbulence corresponding to all the second test structure characteristic groups (1, 6, 8) with a contraction section length of 1.4m is added and recorded as K1 of X1; the turbulence corresponding to all the second test structure characteristic groups (2, 4, 9) with a contraction section length of 2.1m is added and recorded as K2 of X1; the turbulence corresponding to all the second test structure characteristic groups (3, 5, 7) with a contraction section length of 2.8m is added and recorded as K3 of X1. Then: K1 of X1 = 0.097 + 0.093 + 0.064 = 0.254; K2 of X1 = 0.090 + 0.094 + 0.071 = 0.255; K3 of X1 = 0.073 + 0.094 + 0.088 = 0.255; then, calculate the average value of K1 of X1 =0.0847; the average value of K2 of X1 =0.085; the average value of K3 of X1 =0.085; then, calculate the range R of X1: R of X1=0.085-0.0847=0.0003.

[0108] Since the target structural feature with a larger range has a higher importance, the shrinkage ratio X2, shrinkage curve type X3, and shrinkage segment length X1 are analyzed and ranked based on the range R of each target structural feature. The result is: shrinkage ratio X2 > shrinkage curve type X3 > shrinkage segment length X1.

[0109] For intuitive representation, the second reference characteristic quantity of each target structural feature is taken as the horizontal coordinate, and the average turbulence value corresponding to the second reference characteristic quantity of the target structural feature is taken as the vertical coordinate, and the following is constructed: Figure 10 The diagram shown is used to characterize the relationship between the second reference characteristic quantity and turbulence.

[0110] from Figure 10 It can be seen that, based on turbulence as the screening basis, the second reference characteristic value of the contraction ratio is 1:9, the second reference characteristic value of the contraction curve type is BS, and the second reference characteristic value of the contraction section length is 1.4m, which is the first target structural feature group. Based on this, the turbulence corresponding to the first target structural feature group is calculated to be 4.6%. In this way, the turbulence section of the test section affected by the contraction section is reduced by 28%.

[0111] Similarly, by using the above optimization analysis, the second target structural feature group is screened from the L second experimental structural feature groups based on turbulence, and the following can be obtained: Figure 11 The relationship diagram for characterizing the second reference characteristic quantity and speed unevenness is shown in FIG. Figure 11 It can be seen that based on the speed unevenness as the screening basis, the second reference characteristic value of the contraction ratio is 1:5, the second reference characteristic value of the contraction curve type is Vickers, and the second reference characteristic value of the contraction section length is 2m, which is the second target structural feature group. Based on this, the speed unevenness corresponding to the first target structural feature group is calculated to be 1.1%. In this way, the speed unevenness of the test section affected by the contraction section is reduced by 42%.

[0112] The contraction segment structure obtained based on the first target feature group can be referred to Figure 12 The contraction segment structure obtained based on the second target feature group can be referred to in the sub-graph (a) of Figure 12 Subgraph (b) in .

[0113] In another example, the multi-objective genetic algorithm NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm II) can be combined with the goal of minimizing turbulence and velocity non-uniformity, screening the Pareto front solution set from the orthogonal test results, and then determining the target structural feature group based on the Pareto front solution set.

[0114] Step 2406: Determine water tunnel pipeline information of a type of pipeline based on the target structural feature group.

[0115] Therefore, by gradually screening and optimizing the characteristic quantities of M types of target structural features, the optimal design scheme, that is, the target structural feature group with the greatest impact on the flow field quality, can be quickly found from a large number of possible structural feature groups, which greatly reduces the number of experiments and the amount of calculation, and improves the efficiency of determining water tunnel pipeline information.

[0116] In some embodiments of the present application, the water tunnel pipeline information includes the target feature quantity corresponding to each type of target structural feature in the M types of target structural features. Based on this, before the above-mentioned step 2406, the method for determining the contraction section structural information of the water tunnel device may also include: obtaining the measured flow field quality response information of the water tunnel device physical model, and the water tunnel device physical model is constructed based on the target structural feature group.

[0117] Among them, the water tunnel equipment physical model is a scaled-down test model constructed based on the target structural feature group according to the preset proportions and structural requirements of the water tunnel equipment to simulate the actual working conditions of the water tunnel equipment. The scaled-down test model is a model that is reduced by a certain proportion of the actual water tunnel equipment to reduce the test cost and complexity, while ensuring that the water tunnel equipment physical model and the actual water tunnel equipment are similar in fluid dynamics characteristics.

[0118] For example, obtaining the measured flow field quality response information of the water tunnel equipment physical model may specifically include: Particle Image Velocimetry (PIV) is used to obtain the measured flow field quality response information of the water tunnel equipment physical model. PIV is a non-contact fluid velocity measurement method. Tracer particles are seeded into the flow field, illuminated by lasers, and captured by a high-speed camera. The velocity distribution and other information of the flow field are calculated based on the position changes of the particles at different times. The flow field in the test pipeline is then measured, and the measured flow field quality response information of the water tunnel equipment physical model is finally obtained. This measured flow field quality response information can reflect various flow field quality characteristics of the water tunnel equipment physical model during actual operation, such as turbulence and velocity non-uniformity.

[0119] Based on this, the above-mentioned step 2406 can specifically include: when the difference between the measured flow field quality response information and the flow field quality response information corresponding to the target structure feature group is less than or equal to the preset difference, the second reference feature quantity corresponding to each type of target structure feature in the target structure feature group is determined as the target feature quantity corresponding to each type of target structure feature.

[0120] Among them, the preset difference is an allowable error range set in advance according to actual engineering needs and accuracy requirements, and is used to judge the degree of conformity between the measured flow field quality response information and the expected flow field quality response information.

[0121] During the design and optimization process of water tunnel equipment, the flow field quality response information corresponding to the target structural feature set obtained through virtual test pipe model simulation is compared with the actual measured flow field quality response information. When the difference between the two is less than or equal to a preset difference, it means that the simulation results based on the virtual test pipe model are closer to the actual situation. This indicates that the flow field quality of the target structural feature set obtained through the aforementioned series of screening steps in the simulated environment is consistent with the actual measured flow field quality. This consistency can enhance confidence in the simulation results and ensure that the design optimization based on the virtual model has practical application value.

[0122] In some non-limiting embodiments of the present application, when the difference between the measured flow field quality response information and the flow field quality response information corresponding to the target structural feature group is greater than a preset difference, the aforementioned steps 210 to 240 are iteratively performed until the difference between the measured flow field quality response information and the flow field quality response information corresponding to the target structural feature group is less than or equal to the preset difference, thereby obtaining water tunnel pipeline information of a type of pipeline.

[0123] Therefore, by constructing a physical model of the water tunnel equipment and using particle image velocimetry technology to measure the flow field quality information of the test pipeline of the water tunnel equipment physical model, the flow field quality of the water tunnel pipeline physical model can be accurately measured. By comparing the flow field quality response information corresponding to the target structural feature group, the reliability of the design method can be verified, that is, whether the water tunnel pipeline designed based on the target structural feature group can meet the expected flow field quality requirements during actual operation, providing a reliable basis for the design optimization and practical application of water tunnel equipment.

[0124] Based on the method for determining the structural information of the contraction section of a water tunnel device provided in the above embodiment, the present application also provides a specific implementation of an apparatus for determining the structural information of the contraction section of a water tunnel device. Please refer to the following embodiment.

[0125] See also Figure 13 The device 300 for determining the contraction section structure information of a water tunnel device provided in an embodiment of the present application includes: A first acquisition module 310 is configured to acquire at least two candidate structural feature groups for a type of pipe, each candidate structural feature group including N types of candidate structural features and feature quantities corresponding to each type of candidate structural feature, where N is greater than or equal to 3; A first determining module 320 is configured to determine flow field quality response information corresponding to each of the at least two candidate structural feature groups based on the at least two candidate structural feature groups; wherein the flow field quality response information is configured to reflect the quality of the flow field of a test pipeline affected by a type of pipeline constructed by the candidate structural feature groups; A screening module 330 is configured to screen M types of target structural features from N types of candidate structural features according to flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups, where M is greater than or equal to 2; The second determining module 340 is configured to determine water tunnel pipeline information of a type of pipeline according to the M types of target structural features, where the water tunnel pipeline information includes information used to characterize the structure of a type of pipeline.

[0126] Thus, by determining the flow field quality response information corresponding to each of the at least two candidate structural feature groups acquired by the first acquisition module 310 through the first determination module 320, the impact of different structural feature groups on the flow field quality of the test pipeline can be reflected. Subsequently, based on a comprehensive evaluation of the flow field quality response information of multiple candidate structural feature groups, the screening module 330 selects M types of target structural features from N types of candidate structural features, accurately screening multiple types of structural features that have a significant impact on the flow field quality of the water tunnel equipment. Compared to the single-factor design method of screening one by one, this method considers the synergistic effects of multiple structural features, can quickly determine the primary structural features that affect the flow field quality of the test pipeline, avoid multiple invalid experiments on secondary structural features, and effectively improve the efficiency of determining water tunnel pipeline information. The second determination module 340 determines the water tunnel pipeline information of a type of pipeline based on the accurately selected multiple types of target structural features, which can more efficiently and comprehensively optimize the performance of water tunnel equipment, thereby accelerating the research process of the hydrodynamic characteristics of underwater vehicles.

[0127] Each module of the device 300 for determining the contraction section structure information of the water tunnel device provided in the embodiment of the present application can realize Figure 1 The functions of the various steps of the method for determining the structural information of the contraction section of the water tunnel device are provided, and the corresponding technical effects can be achieved. For the sake of brevity, they are not repeated here.

[0128] In some embodiments of the present application, the first determining module 320 in the embodiments of the present application may specifically include: A construction submodule is used to construct a virtual water tunnel device model corresponding to each candidate structural feature group in at least two candidate structural feature groups, wherein the virtual water tunnel device model includes a virtual pipe model and a virtual test pipe model; The first determination submodule is used to perform a steady-state simulation on the virtual water tunnel equipment model through a virtual fluid environment to obtain flow field information of virtual position points of the virtual test pipeline model, where the flow field information is used to represent the flow state of the fluid flowing through the virtual test pipeline model at the virtual position points; The second determining submodule is configured to determine flow field quality response information based on flow field information of the virtual position point by using a flow field quality detection algorithm corresponding to the virtual test pipeline model.

[0129] In some embodiments of the present application, the second determining submodule in the embodiments of the present application may be specifically used to: In the case where the flow field quality response information includes turbulence and velocity non-uniformity, and the flow field quality detection algorithm includes a turbulence detection algorithm and a velocity non-uniformity detection algorithm, the turbulence is determined based on the flow field information of the virtual position point using the turbulence detection algorithm corresponding to the virtual test pipeline model; the turbulence is used to characterize the pulsation intensity of the fluid velocity of the virtual test pipeline model in the virtual fluid environment; The velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model is used to determine the velocity non-uniformity based on the flow field information of the virtual position points. The velocity non-uniformity is used to characterize the degree of non-uniformity in the spatial distribution of the fluid velocity of the virtual test pipeline model in the virtual fluid environment.

[0130] In some embodiments of the present application, the screening module 330 in the embodiments of the present application may specifically include: A third determination submodule is configured to determine a sensitivity value corresponding to each candidate structural feature based on flow field quality response information corresponding to each candidate structural feature group in at least two candidate structural feature groups; the sensitivity value is used to characterize the degree of influence of the candidate structural feature on the flow field quality of the test pipeline; The first screening submodule is used to screen M types of target structural features from N types of candidate structural features according to the sensitivity value corresponding to each candidate structural feature, and the sensitivity value of the target structural feature is greater than or equal to the preset sensitivity value.

[0131] In some embodiments of the present application, the third determination submodule in the embodiments of the present application may be specifically used to: determining, based on the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups, a relationship between the N types of candidate structural features and the flow field quality response information; According to the relationship between the N types of candidate structural features and the flow field quality response information, a sensitivity value corresponding to each candidate structural feature is determined.

[0132] In some embodiments of the present application, the second determining module 340 in the embodiments of the present application may specifically include: A fourth determination submodule is configured to determine, when the water tunnel pipeline information includes target feature quantities corresponding to each type of target structure features in M types of target structure features, P first reference feature quantities corresponding to each type of target structure features according to the feature quantities corresponding to each type of target structure features in the M types of target structure features; A first combining submodule is configured to randomly combine the P first reference feature quantities corresponding to each type of target structural feature in the M types of target structural features to obtain L first test structural feature groups; a second screening submodule, configured to screen Q second reference feature quantities from the P first reference feature quantities corresponding to each type of target structural feature according to the flow field quality information corresponding to each first test structural feature group in the L first test structural feature groups; The second combination submodule is used to combine the Q second reference feature quantities corresponding to each type of target structural feature in the M types of target structural features through an orthogonal experimental design method to generate L second experimental structural feature groups; a third screening submodule, configured to screen a target structural feature group from the L second test structural feature groups according to flow field quality response information corresponding to each second test structural feature group in the L second test structural feature groups; The fifth determining submodule is configured to determine the water tunnel pipeline information of the first type of pipeline according to the target structural feature group.

[0133] In some embodiments of the present application, the device for determining the contraction section structural information of the water tunnel device in the embodiment of the present application may further include: The second acquisition module is used to obtain measured flow field quality response information of a water tunnel equipment entity model before determining the water tunnel pipeline information of the first type of pipeline based on the target structural feature group when the water tunnel pipeline information includes the target feature quantity corresponding to each type of target structural feature in the M types of target structural features and the water tunnel pipeline information of the first type of pipeline is determined according to the target structural feature group. The water tunnel equipment entity model is constructed based on the target structural feature group.

[0134] The above-mentioned second determination module 340 can be used to: when the difference between the measured flow field quality response information and the flow field quality response information corresponding to the target structure feature group is less than or equal to the preset difference, determine the second reference feature quantity corresponding to each type of target structure feature in the target structure feature group as the target feature quantity corresponding to each type of target structure feature.

[0135] Figure 14 A schematic diagram of the hardware structure of an electronic device provided in some embodiments of the present application is shown.

[0136] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0137] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0138] Memory 402 may include a large-capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, memory 402 is a non-volatile solid-state memory.

[0139] In certain embodiments, the memory 402 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, typically, the memory 402 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the data processing method according to the first aspect of the present application.

[0140] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any one of the methods for determining the structural information of the contraction section of the water tunnel device in the above embodiments.

[0141] In one example, the electronic device may further include a communication interface 403 and a bus 410. Figure 9 As shown, the processor 401 , the memory 402 , and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0142] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0143] Bus 410 includes hardware, software, or both that couples components of an electronic device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0144] The electronic device can execute the method for determining the contraction section structure information of the water tunnel device in the embodiment of the present application, thereby realizing the combination Figures 2 to 8 A method and device for determining structural information of a contraction section of a water tunnel device are described.

[0145] In addition, in conjunction with the method for determining the structural information of the contraction section of the water tunnel device in the above-mentioned embodiment, the embodiment of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the methods for determining the structural information of the contraction section of the water tunnel device in the above-mentioned embodiment is implemented. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, and the like.

[0146] In addition, in conjunction with the methods for determining the structural information of the contraction section of a water tunnel device in the above-described embodiments, embodiments of the present application may provide a computer program product for implementation. This program product is stored in a storage medium and may specifically include a computer program or instructions. When executed by a processor, the computer program or instructions implement any of the methods for determining the structural information of the contraction section of a water tunnel device in the above-described embodiments. This program product is executed by at least one processor to implement the various processes of the above-described methods for determining the structural information of the contraction section of a water tunnel device, achieving the same technical effects. To avoid repetition, these are not further described here.

[0147] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0148] The functional blocks shown in the above block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memory, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. Code segments can be downloaded via a computer network such as the Internet or an intranet.

[0149] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0150] Aspects of the present disclosure have been described above with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine such that execution of these instructions by the processor of the computer or other programmable data processing device enables the implementation of the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, as well as combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0151] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. A method for determining structural information of a contraction section of a water tunnel device, characterized in that: The water tunnel device includes a type of pipeline and a test pipeline associated with the type of pipeline, wherein the structure of the type of pipeline affects the flow field quality of the test pipeline, the type of pipeline is a contraction section, and the test pipeline is a test section; the method includes: Obtain at least two candidate structural feature groups for the type of pipe, each candidate structural feature group including N types of candidate structural features and feature quantities corresponding to each type of candidate structural features, where N is greater than or equal to 3; Determining flow field quality response information corresponding to each of the at least two candidate structural feature groups based on the at least two candidate structural feature groups; wherein the flow field quality response information is used to reflect the quality of the flow field of a test pipeline affected by a type of pipeline constructed by the candidate structural feature groups; screening M types of target structural features from the N types of candidate structural features according to the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups, where M is greater than or equal to 2; According to the M types of target structural features, water tunnel pipeline information of the first type of pipeline is determined, where the water tunnel pipeline information includes information used to characterize the first type of pipeline structure.

2. The method according to claim 1, characterized in that The determining, based on the at least two candidate structural feature groups, flow field quality response information corresponding to each of the at least two candidate structural feature groups includes: Constructing a virtual water tunnel device model corresponding to each candidate structural feature group in at least two candidate structural feature groups, wherein the virtual water tunnel device model includes a virtual pipeline model and a virtual test pipeline model; Performing a steady-state simulation on the virtual water tunnel equipment model through a virtual fluid environment to obtain flow field information of a virtual position point of the virtual test pipeline model, wherein the flow field information is used to represent information on the flow state of the fluid flowing through the virtual test pipeline model at the virtual position point; The flow field quality response information is determined according to the flow field information of the virtual position point using a flow field quality detection algorithm corresponding to the virtual test pipeline model.

3. The method according to claim 2, characterized in that The flow field quality response information includes turbulence and velocity non-uniformity, and the flow field quality detection algorithm includes a turbulence detection algorithm and a velocity non-uniformity detection algorithm; The flow field quality detection algorithm corresponding to the virtual test pipeline model is used to determine the flow field quality response information according to the flow field information of the virtual position point, including: Determining the turbulence according to the flow field information of the virtual position point using a turbulence detection algorithm corresponding to the virtual test pipeline model; the turbulence is used to characterize the pulsation intensity of the fluid velocity of the virtual test pipeline model in the virtual fluid environment; The velocity non-uniformity is determined based on the flow field information of the virtual position point using a velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model; the velocity non-uniformity is used to characterize the degree of non-uniformity in the spatial distribution of the fluid velocity of the virtual test pipeline model in the virtual fluid environment.

4. The method according to claim 1, wherein The step of screening M types of target structural features from the N types of candidate structural features according to the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups includes: Determining a sensitivity value corresponding to each candidate structural feature according to flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups; the sensitivity value is used to characterize the degree of influence of the candidate structural feature on the flow field quality of the test pipeline; According to the sensitivity value corresponding to each candidate structural feature, M types of target structural features are screened from the N types of candidate structural features, and the sensitivity value of the target structural feature is greater than or equal to a preset sensitivity value.

5. The method according to claim 4, characterized in that The determining, based on the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups, a sensitivity value corresponding to each candidate structural feature, includes: determining, based on the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups, a relationship between the N types of candidate structural features and the flow field quality response information; According to the relationship between the N types of candidate structural features and the flow field quality response information, a sensitivity value corresponding to each candidate structural feature is determined.

6. The method according to claim 1, characterized in that The water tunnel pipeline information includes a target feature quantity corresponding to each type of target structure feature in M types of target structure features; and determining the water tunnel pipeline information of the type of pipeline according to the M types of target structure features includes: Determine, based on the feature quantity corresponding to each type of target structure feature in the M types of target structure features, P first reference feature quantities corresponding to each type of target structure feature; Randomly combining P first reference feature quantities corresponding to each type of target structural feature in the M types of target structural features to obtain L first test structural feature groups; Selecting Q second reference feature quantities from the P first reference feature quantities corresponding to each type of target structural feature according to the flow field quality information corresponding to each first test structural feature group in the L first test structural feature groups; Through the orthogonal experimental design method, the Q second reference feature quantities corresponding to each type of target structural feature in the M types of target structural features are combined to generate L second experimental structural feature groups; screening a target structural feature group from the L second test structural feature groups according to flow field quality response information corresponding to each second test structural feature group in the L second test structural feature groups; The water tunnel pipeline information of the first type of pipeline is determined according to the target structural feature group.

7. The method according to claim 6, characterized in that The water tunnel pipeline information includes the target feature quantity corresponding to each type of target structure feature in the M types of target structure features; Before determining the water tunnel pipeline information of the first type of pipeline according to the target structural feature group, the method further includes: Acquiring measured flow field quality response information of a water tunnel device entity model, wherein the water tunnel device entity model is constructed based on the target structural feature group; Determining the water tunnel pipeline information of the first type of pipeline according to the target structural feature group includes: When the difference between the measured flow field quality response information and the flow field quality response information corresponding to the target structure feature group is less than or equal to a preset difference, the second reference feature quantity corresponding to each type of target structure feature in the target structure feature group is determined as the target feature quantity corresponding to each type of target structure feature.

8. A device for determining structural information of a contraction section of a water tunnel device, characterized in that: The water tunnel device includes a type of pipeline and a test pipeline associated with the type of pipeline. The structure of the type of pipeline affects the flow field quality of the test pipeline. The type of pipeline is a contraction section, and the test pipeline is a test section. The device includes: A first acquisition module is configured to acquire at least two candidate structural feature groups for the type of pipe, each candidate structural feature group including N types of candidate structural features and feature quantities corresponding to each type of candidate structural features, where N is greater than or equal to 3; A first determining module is configured to determine, based on the at least two candidate structural feature groups, flow field quality response information corresponding to each of the at least two candidate structural feature groups; wherein the flow field quality response information is configured to reflect the quality of the flow field of a test pipeline affected by a type of pipeline constructed by the candidate structural feature groups; a screening module, configured to screen M types of target structural features from the N types of candidate structural features according to flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups, where M is greater than or equal to 2; The second determining module is used to determine the water tunnel pipeline information of the first type of pipeline according to the M types of target structural features, where the water tunnel pipeline information includes information used to characterize the first type of pipeline structure.

9. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for determining the contraction section structural information of the water tunnel equipment according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for determining the contraction section structural information of the water tunnel device according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining the contraction section structural information of a water tunnel device according to any one of claims 1 to 7.

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