Method for determining structure information of a contraction section of a water tunnel device and related product

Abstract

The application discloses a contraction section structure information determination method of a water tunnel device and related products, and is applied to the technical field of information processing. The method comprises the following steps: acquiring at least two candidate structure characteristic groups of a type of pipeline; determining flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups according to the at least two candidate structure characteristic groups; screening M types of target structure characteristics from N types of candidate structure characteristics according to the flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups; and determining water tunnel pipeline information of the type of pipeline according to the M types of target structure characteristics. The method can determine the water tunnel pipeline information of the type of pipeline based on the accurately screened multiple types of target structure characteristics, can more efficiently and comprehensively realize performance optimization of the water tunnel device, and can further accelerate the research process of the hydrodynamic characteristics of an underwater vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of information processing, and particularly relates to a method for determining structure information of a contraction section of a water tunnel device and related products. BACKGROUND

[0002] The water tunnel device is a device for studying hydrodynamic characteristics of underwater vehicles and can be applied to the shape design of underwater vehicles. The water tunnel device includes a pipeline and a test pipeline associated with the pipeline. The pipeline includes a straightener section, a contraction section, a diffuser section, and a pressure regulating section. The test pipeline includes a test section. The structure of the pipeline affects the flow field quality of the test pipeline, and the quality of the flow field affects the accuracy of the results of the study of the hydrodynamic characteristics of the underwater vehicles.

[0003] In related technologies, the structure characteristics of the pipeline can be determined by a single factor design method. The structure characteristics of the contraction section include, but are not limited to, a contraction ratio, a curve type, and a pipeline length. For example, the single factor design method can determine the structure characteristics of the contraction pipeline by controlling the contraction ratio while keeping the curve type and the pipeline length unchanged. However, the efficiency of determining the pipeline information of the water tunnel device based on the foregoing method is low, which affects the progress of the study of the hydrodynamic characteristics of the underwater vehicles. SUMMARY

[0004] Embodiments of the present application provide a method for determining structure information of a contraction section of a water tunnel device and related products. Specifically, the related products can include a device, equipment, medium, and program product for determining structure information of a contraction section of a water tunnel device, which can solve the problem of low efficiency of determining pipeline information of a water tunnel device by a single factor design method.

[0005] In a first aspect, a method for determining structure information of a contraction section of a water tunnel device is provided. The water tunnel device includes a pipeline and a test pipeline associated with the pipeline. The structure of the pipeline affects the flow field quality of the test pipeline. The pipeline is a contraction section, and the test pipeline is a test section. The method includes the following steps:

[0006] obtaining at least two candidate structure characteristic groups of the pipeline, each candidate structure characteristic group including N candidate structure characteristics and a characteristic quantity corresponding to each candidate structure characteristic, and N being greater than or equal to 3;

[0007] determining flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups according to the at least two candidate structure characteristic groups. The flow field quality response information reflects the degree of the quality of the flow field of the test pipeline affected by the pipeline constructed by the candidate structure characteristic group;

[0008] screening M target structural features from the N candidate structural features according to the flow field quality response information corresponding to each of the at least two candidate structural feature groups, M being greater than or equal to 2;

[0009] determining water tunnel pipeline information of a type of pipeline according to the M target structural features, the water tunnel pipeline information including information for representing a structure of the type of pipeline.

[0010] In some possible implementation manners of the embodiments of the present application, determining the flow field quality response information corresponding to each of the at least two candidate structural feature groups according to the at least two candidate structural feature groups includes:

[0011] constructing a virtual water tunnel equipment model corresponding to each of the at least two candidate structural feature groups, the virtual water tunnel equipment model including a type of virtual pipeline model and a virtual test pipeline model;

[0012] performing 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, the flow field information being information for representing a flow state of fluid flowing through the virtual test pipeline model at the virtual position point;

[0013] determining the flow field quality response information according to the flow field information of the virtual position point through a flow field quality detection algorithm corresponding to the virtual test pipeline model.

[0014] In some possible implementation manners of the embodiments of the present application, the flow field quality response information includes turbulence intensity and velocity non-uniformity, and the flow field quality detection algorithm includes a turbulence intensity detection algorithm and a velocity non-uniformity detection algorithm;

[0015] determining the flow field quality response information according to the flow field information of the virtual position point through the flow field quality detection algorithm corresponding to the virtual test pipeline model, including:

[0016] determining turbulence intensity according to the flow field information of the virtual position point through a turbulence intensity detection algorithm corresponding to the virtual test pipeline model, the turbulence intensity being used to represent an intensity of fluctuation of fluid velocity in the virtual fluid environment of the virtual test pipeline model;

[0017] determining velocity non-uniformity according to the flow field information of the virtual position point through a velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model, the velocity non-uniformity being used to represent a degree of non-uniformity of spatial distribution of fluid velocity in the virtual fluid environment of the virtual test pipeline model.

[0018] In some possible implementation manners of the embodiments of the present application, screening the M target structural features from the N candidate structural features according to the flow field quality response information corresponding to each of the at least two candidate structural feature groups includes:

[0019] determine a sensitivity value corresponding to each candidate structural feature according to the 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 represent an influence degree of the candidate structural feature on the flow field quality of the test pipeline;

[0020] select M target structural features from the N candidate structural features according to the sensitivity value corresponding to each candidate structural feature, the sensitivity value of the target structural feature being greater than or equal to a preset sensitivity value.

[0021] In some possible implementation manners of the embodiments of the present application, the determining of the sensitivity value corresponding to each candidate structural feature 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:

[0022] determining a relationship between the N candidate structural features and the flow field quality response information according to the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups;

[0023] determining the sensitivity value corresponding to each candidate structural feature according to the relationship between the N candidate structural features and the flow field quality response information.

[0024] In some possible implementation manners of the embodiments of the present application, the water tunnel pipeline information includes a target feature quantity corresponding to each target structural feature in the M target structural features; the determining of the water tunnel pipeline information of the pipeline of the type according to the M target structural features includes:

[0025] determining P first reference feature quantities corresponding to each target structural feature in the M target structural features according to the target feature quantity corresponding to each target structural feature in the M target structural features;

[0026] randomly combining the P first reference feature quantities corresponding to each target structural feature in the M target structural features to obtain L first test structural feature groups;

[0027] selecting Q second reference feature quantities from the P first reference feature quantities corresponding to each 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;

[0028] combining the Q second reference feature quantities corresponding to each target structural feature in the M target structural features by using an orthogonal test design method to generate L second test structural feature groups;

[0029] selecting a target structural feature group from the L second test structural feature groups according to the flow field quality response information corresponding to each second test structural feature group in the L second test structural feature groups;

[0030] According to the target structure feature group, water tunnel pipe information of a type of pipe is determined.

[0031] In some possible implementation manners of the embodiments of the present application, the water tunnel pipe information includes a target feature quantity corresponding to each type of target structure feature in the M types of target structure features.

[0032] Before the water tunnel pipe information of a type of pipe is determined according to the target structure feature group, the method further includes:

[0033] Obtaining measured flow field quality response information of a water tunnel equipment physical model, the water tunnel equipment physical model being constructed based on the target structure feature group;

[0034] According to the target structure feature group, water tunnel pipe information of a type of pipe is determined, including:

[0035] In a case where a difference between the measured flow field quality response information and flow field quality response information corresponding to the target structure feature group is less than or equal to a preset difference, a second reference feature quantity corresponding to each type of target structure feature in the target structure feature group is determined as a target feature quantity corresponding to each type of target structure feature.

[0036] In a second aspect, the embodiments of the present application provide a water tunnel equipment contraction section structure information determination device, the water tunnel equipment including a type of pipe and a test pipe associated with the type of pipe, a structure of the type of pipe affecting a flow field quality of the test pipe, the type of pipe being a contraction section, and the test pipe being a test section; the water tunnel equipment contraction section structure information determination device including:

[0037] The first obtaining module is configured to obtain at least two candidate structure feature groups of a type of pipe, each candidate structure feature group including N types of candidate structure features and a feature quantity corresponding to each type of candidate structure feature, N being greater than or equal to 3;

[0038] The first determination module is configured to determine flow field quality response information corresponding to each candidate structure feature group in the at least two candidate structure feature groups according to the at least two candidate structure feature groups; wherein the flow field quality response information is used to reflect a degree of advantage or disadvantage of a flow field quality of a test pipe affected by a type of pipe constructed by the candidate structure feature group;

[0039] The screening module is configured to screen M types of target structure features from the N types of candidate structure features according to the flow field quality response information corresponding to each candidate structure feature group in the at least two candidate structure feature groups, M being greater than or equal to 2;

[0040] The second determination module is configured to determine water tunnel pipe information of a type of pipe according to the M types of target structure features, the water tunnel pipe information including information used to represent a structure of the type of pipe.

[0041] In a third aspect, an electronic device is provided, and the device includes a processor and a memory storing computer program instructions; the processor implements the method for determining the structure information of the contraction section of the water tunnel device according to any one of the first aspect when executing the computer program instructions.

[0042] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer program instructions; the computer program instructions are executed by a processor to implement the method for determining the structure information of the contraction section of the water tunnel device according to any one of the first aspect.

[0043] In a fifth aspect, a computer program product is provided, and the computer program product includes computer programs or instructions; the computer programs or instructions are executed by a processor to implement the method for determining the structure information of the contraction section of the water tunnel device according to any one of the first aspect.

[0044] The method for determining the structure information of the contraction section of the water tunnel device and related products provided by the embodiments of the present application can reflect the influence of different structure characteristic groups on the flow field quality of the test pipeline by determining the flow field quality response information corresponding to each candidate structure characteristic group in at least two candidate structure characteristic groups. Then, based on the comprehensive evaluation of the flow field quality response information of the multiple candidate structure characteristic groups, M target structure characteristic groups are selected from N candidate structure characteristic groups, which can accurately select multiple structure characteristic groups that have a greater influence on the flow field quality of the water tunnel device. Compared with the one-by-one checking method of the single factor design method, this method considers the synergistic effect of multiple structure characteristics, can quickly determine the main structure characteristics that affect the flow field quality of the test pipeline, avoids multiple invalid tests on the secondary structure characteristics, and effectively improves the determination efficiency of the water tunnel pipeline information. Based on the accurate selection of the multiple target structure characteristic groups, the water tunnel pipeline information of a type of pipeline can be determined, which can more efficiently and comprehensively optimize the performance of the water tunnel device, and further accelerate the research process of the hydrodynamic characteristics of the underwater vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. Those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0046] Figure 1 A structural schematic diagram of a water tunnel device provided by some embodiments of the present application is shown

[0047] Figure 2 A flowchart of a method for determining the structure information of the contraction section of a water tunnel device provided by some embodiments of the present application is shown

[0048] Figure 3A flow chart illustrating one specific implementation of step 220 provided by some embodiments of the present application is shown;

[0049] Figure 4 A flow chart illustrating one specific implementation of step 2203 provided by some embodiments of the present application is shown;

[0050] Figure 5 A flow chart illustrating one specific implementation of step 230 provided by some embodiments of the present application is shown;

[0051] Figure 6 A flow chart illustrating one specific implementation of step 2301 provided by some embodiments of the present application is shown;

[0052] Figure 7 A flow chart illustrating one specific implementation of step 240 provided by some embodiments of the present application is shown;

[0053] Figure 8 A structural diagram of a contraction section structure corresponding to a second test structural feature group provided by some embodiments of the present application is shown;

[0054] Figure 9 A variation trend diagram of a turbulent flow section of a water tunnel pipeline corresponding to a second test structural feature group provided by some embodiments of the present application is shown;

[0055] Figure 10 A relationship diagram for characterizing a second reference feature quantity and a turbulent flow degree provided by some embodiments of the present application is shown;

[0056] Figure 11 A relationship diagram for characterizing a second reference feature quantity and a velocity unevenness degree provided by some embodiments of the present application is shown;

[0057] Figure 12 A structural diagram of a contraction section structure corresponding to a target feature group provided by some embodiments of the present application is shown;

[0058] Figure 13 A structural diagram of a contraction section structure information determination apparatus of a water tunnel device provided by some embodiments of the present application is shown;

[0059] Figure 14 A structural diagram of an electronic device provided by some embodiments of the present application is shown. DETAILED DESCRIPTION

[0060] The features and exemplary embodiments of the various aspects of the present application will be described in detail below with reference to the drawings. For the purpose of clarity, the description is divided into the following sections: technical scheme, technical effects, and specific embodiments. The specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. The present application can be implemented without some of the specific details described below. The description of the embodiments below is merely intended to provide a better understanding of the present application by showing examples of the present application.

[0061] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by an "includes" statement does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0062] It should be noted that the acquisition, storage, use and processing of data in the embodiments of the present application comply with the relevant provisions of national laws and regulations.

[0063] It should be noted that in the embodiments of the present application, some software, components, models and other industry existing solutions may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present application, but does not mean that the applicant has or will necessarily use the solution.

[0064] Before describing the technical solutions provided by the embodiments of the present application, in order to facilitate the understanding of the embodiments of the present application, the present application first specifically describes the related technologies involved:

[0065] As shown in Figure 1 The water tunnel device includes a test section 101, a straightening section 102, a contraction section 103, a diffusion section 104 and a pressure regulating section 105. The straightening section 102, the contraction section 103, the test section 101, the diffusion section 104 and the pressure regulating section 105 are connected in sequence. The water flow flows from the inlet of the straightening section 102, passes through the contraction section 103, the test section 101 and the diffusion section 104, and then flows out from the outlet of the pressure regulating section 105. Each pipe section cooperates to affect the flow field quality in the test section 101.

[0066] The rectification section 102 serves as a water flow preprocessing unit and is internally provided with a honeycomb and a damping net double structure. The honeycomb divides large-scale vortices into small-scale vortices, thereby reducing the initial intensity of the turbulent flow. The damping net further weakens the turbulent flow by impeding the movement of the water flow and accelerating energy dissipation. The contraction section 103 serves as a water flow regulation component. The contraction ratio, the curve shape of the contraction section, and the length parameters of the contraction section affect the turbulent flow suppression effect. A larger contraction ratio can reduce the turbulent flow degree. A reasonable contraction section curve design can reduce boundary layer separation and avoid vortex generation. The length of the contraction section affects the smoothness of the fluid acceleration process. The diffusion section 104 gradually converts the kinetic energy of the high-speed water flow into pressure energy by expanding the flow passage cross-sectional area, thereby avoiding pressure fluctuations and turbulent flow resurgence in the downstream due to sudden changes in flow velocity. The pressure regulating section 105 regulates the outlet pressure to maintain the pressure balance of the entire water tunnel device.

[0067] The performance optimization of the water tunnel device depends on the reasonable design of the pipe, which directly determines the flow field quality of the test section 101. The quality of the flow field directly affects the accuracy of the research results of the hydrodynamic characteristics of the underwater vehicle. In the related art, the structural characteristics of the pipe can be determined by a single factor design method. The structural characteristics of the contraction section 103 can include but are not limited to the contraction ratio, the curve type, and the pipe length. For example, the single factor design method can determine the structural characteristics of the contraction pipe by controlling the contraction ratio while keeping the curve type and the pipe length unchanged. Since the single factor design method can only explore the influence of one variable on the flow field quality at a time, if the influence of multiple factors such as the contraction ratio, the curve type, and the pipe length on the flow field quality of the test pipe is to be comprehensively understood, a test needs to be redesigned and performed every time a variable is changed, which consumes a lot of time and resources, making the efficiency of determining the pipe information of the water tunnel device based on this method extremely low, and thus seriously delaying the progress of the research on the hydrodynamic characteristics of the underwater vehicle.

[0068] To solve the problems in the related art described above, the embodiments of the present application provide a method for determining the structural information of a contraction section of a water tunnel device and related products. The method for determining the structural information of the contraction section of the water tunnel device provided by the embodiments of the present application will be described in detail below with reference to the specific embodiments and application scenarios. Figure 2 to the accompanying drawings Figure 7 The method for determining the structural information of the contraction section of the water tunnel device provided by the embodiments of the present application will be described in detail below with reference to the specific embodiments and application scenarios.

[0069] Figure 1 A flowchart of a method for determining the structural information of a contraction section of a water tunnel device provided by some embodiments of the present application is shown. The water tunnel device can include a pipe and a test pipe associated with the pipe. The structure of the pipe affects the flow field quality of the test pipe. The pipe is at least one of a rectification section, a contraction section, a diffusion section, and a pressure regulating section. The test pipe is a test section. The embodiments of the present application exemplarily illustrate the pipe as the contraction section and the test pipe as the test section. As shown in the figure, the method for determining the structural information of the contraction section of the water tunnel device includes the following steps. Figure 1As shown, the method for determining the structure information of the contraction section of the water tunnel device can include steps 210 to 240.

[0070] Step 210: Obtain at least two candidate structure feature groups of a type of pipeline, each candidate structure feature group including N types of candidate structure features and a feature quantity corresponding to each type of candidate structure feature, N being greater than or equal to 3.

[0071] Step 220: Determine the flow field quality response information corresponding to each candidate structure feature group in the at least two candidate structure feature groups according to the at least two candidate structure feature groups; wherein the flow field quality response information is used to reflect the degree of advantage or disadvantage of the flow field quality of the test pipeline affected by the candidate structure feature group;

[0072] Step 230: Select M types of target structure features from the N types of candidate structure features according to the flow field quality response information corresponding to each candidate structure feature group in the at least two candidate structure feature groups, M being greater than or equal to 2.

[0073] Step 240: Determine the water tunnel pipeline information of the type of pipeline according to the M types of target structure features, the water tunnel pipeline information including information for characterizing the structure of the type of pipeline.

[0074] Thus, by determining the flow field quality response information corresponding to each candidate structure feature group in the at least two candidate structure feature groups, the influence of different structure 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 the multiple candidate structure feature groups, the M types of target structure features are selected from the N types of candidate structure features, which can accurately select multiple types of structure features that have a greater influence on the flow field quality of the water tunnel device. Compared with the single-factor design method of checking one by one, this method considers the synergistic effect of multiple structure features, can quickly determine the main structure features that affect the flow field quality of the test pipeline, avoids multiple invalid tests on secondary structure features, and effectively improves the determination efficiency of the water tunnel pipeline information. Based on the accurately selected multiple types of target structure features, the water tunnel pipeline information of the type of pipeline is determined, which can more efficiently and comprehensively realize the performance optimization of the water tunnel device, and thus accelerate the research process of the hydrodynamic characteristics of the underwater vehicle.

[0075] The above steps are described in detail as follows.

[0076] First, before step 210 is performed, at least two candidate structure feature groups in step 210 need to be determined. Based on this, the method for determining the structure information of the contraction section of the water tunnel device can further include:

[0077] An initial structure feature set is obtained, the initial structure feature set including N types of candidate structure features and reference feature quantities corresponding to each type of candidate structure features; at least two feature quantities corresponding to each type of candidate structure features are determined according to the reference feature quantities corresponding to each type of candidate structure features in the N types of candidate structure features; and at least two candidate structure feature groups are determined according to the N types of candidate structure features and the at least two feature quantities corresponding to each type of candidate structure features.

[0078] Taking a type of pipeline as a contraction section as an example, the N types of candidate structure features can include a contraction section length, a contraction ratio, a contraction curve type, an inlet shape, an outlet shape, and a number of contraction surfaces. The reference feature quantity corresponding to the contraction section length can include but is not limited to 0.8 meters to 2.4 meters; the reference feature quantity corresponding to the contraction ratio can include but is not limited to 1:3~1:9; the reference feature quantity corresponding to the contraction curve type can include but is not limited to a quintic, a Weierstrass, and a Batchelor-Shaw (B-S) curve; the reference feature quantity corresponding to the inlet shape can include but is not limited to a square and a circle; the reference feature quantity corresponding to the outlet shape can include but is not limited to a square and a circle; and the reference feature quantity corresponding to the number of contraction surfaces can include but is not limited to 3 surfaces and 4 surfaces. Further, in order to more intuitively and accurately analyze the influence of different structure features on the performance of the contraction section, an initial three-dimensional model of the contraction section can also be constructed based on the aforementioned N types of candidate structure features and the reference feature quantities corresponding to each type of candidate structure features. Through three-dimensional modeling, abstract structure parameters can be converted into visual geometric shapes, facilitating rapid evaluation of the spatial layout and morphological differences of the contraction section under different feature combinations.

[0079] Exemplarily, the at least two feature quantities corresponding to each type of candidate structure feature can be determined by a tester based on the reference feature quantity corresponding to each type of candidate structure feature in the aforementioned N types of candidate structure features. After the reference feature quantity corresponding to each type of candidate structure feature is determined, at least two candidate structure feature groups can be generated by Plackett-Burman design (PBD), and the specific steps are as follows:

[0080] For the N types of candidate structure features, the N types of candidate structure features are taken as test factors, and high (+1) and low (-1) levels are set for the reference feature quantity corresponding to each test factor, i.e., each type of candidate structure feature. Specifically, refer to Table 1 below.

[0081]

[0082] Table 1

[0083] Then, a partial analysis factor test scheme is generated using Plackett-Burman design, and the number of tests is N=k+1 (k is the number of test factors). In this way, a series of test combinations can be obtained, each combination corresponding to a candidate structure feature group.

[0084] It is worth mentioning here that the level setting of the characteristic quantity of each type of candidate structure is not limited to the above two types, and the level can be flexibly set according to actual needs and research accuracy, which can be set to three levels, four levels, etc., to capture the influence of structural feature changes on the flow field quality of the test pipeline in more detail, and the present application does not make specific limitations.

[0085] Secondly, it relates to step 220, and the flow field quality response information can include turbulence intensity and velocity non-uniformity. Among them, the turbulence intensity is used to characterize the intensity of the fluid velocity fluctuation in the flow field of the test pipeline, and the velocity non-uniformity is used to reflect the non-uniformity of the spatial distribution of the fluid velocity in the flow field of the test pipeline.

[0086] Among them, the higher the turbulence intensity, the stronger the disorder of the fluid motion in the test pipeline, and the lower the velocity non-uniformity, the more uniform the fluid velocity distribution in the test pipeline.

[0087] In some embodiments of the present application, in order to accurately determine the flow field quality response information corresponding to each candidate structure feature group, as shown in Figure 3 The step 220 can specifically include steps 2201 to 2203.

[0088] Step 2201, constructing a virtual water tunnel equipment model corresponding to each candidate structure feature group in at least two candidate structure feature groups, and the virtual water tunnel equipment model includes a virtual pipeline model and a virtual test pipeline model.

[0089] Exemplarily, first, a first virtual pipeline model is constructed, which can specifically include, for each candidate structure feature group, constructing a virtual pipeline model according to N types of candidate structure features in the candidate structure feature group and the characteristic quantity corresponding to each type of candidate structure feature, for example, constructing a virtual pipeline model according to N types of candidate structure features such as contraction section length, contraction ratio, contraction curve type, inlet shape, outlet shape and number of contraction surfaces, and the specific characteristic quantity corresponding to the N types of candidate structure features. The three-dimensional modeling software is used. Secondly, a virtual test pipeline model is obtained, which can be pre-constructed based on the standard size and physical characteristics of the actual water tunnel test pipeline. It can be understood that the virtual test pipeline model can have a fixed parameter setting to ensure the consistency and comparability of the test environment in the evaluation process of different candidate structure feature groups. Then, a virtual water tunnel equipment model is constructed by connecting a virtual pipeline model and a virtual test pipeline model. Specifically, the outlet of the virtual pipeline model can be connected with the inlet of the virtual test pipeline model, and the fluid flows from the virtual pipeline model into the virtual test pipeline model, so the structural features of the virtual pipeline model will directly affect the state of the fluid flowing into the virtual test pipeline model.

[0090] It can be understood that the change of the structural characteristics of the virtual pipeline model of one type will cause the change of the physical quantities such as the velocity, pressure and turbulence intensity of the fluid flowing into the virtual test pipeline model, thereby affecting the flow field quality in the virtual test pipeline model.

[0091] In step 2202, a steady-state simulation is performed on the virtual water tunnel equipment model through the virtual fluid environment to obtain flow field information of the virtual test pipeline model at the virtual position point, and the flow field information is used to represent the information of the flow state of the fluid flowing through the virtual test pipeline model at the virtual position point.

[0092] The virtual fluid environment can be a preset boundary condition, which can specifically include an inlet condition, an outlet condition and a wall condition designed for the virtual water tunnel equipment model. For example, the inlet condition can be set as a uniform flow, the turbulence intensity is set to 10%, which represents the fluctuation degree 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 turbulent length scale is set to 7% of the length of the inlet, which reflects the average size of the turbulent vortex. The outlet condition can be set as a pressure outlet, and the pressure value is set to 0, which simulates the state of the outlet being connected to the atmosphere, and ensures that the fluid can flow out of the calculation area smoothly. The wall condition can use a no-slip boundary condition for all walls, that is, the fluid velocity at the wall is 0.

[0093] Exemplarily, first, the built virtual water tunnel equipment model is imported into the computational fluid dynamics software, and the environment parameters of the virtual fluid environment are set in the computational fluid dynamics software to perform transient simulation on the virtual water tunnel equipment model. Then, the grid division tool in the computational fluid dynamics software is used to discretize the calculation region of the virtual water tunnel equipment model. The virtual water tunnel equipment model is divided into structured hexahedral elements, and local encryption processing is performed on complex flow regions such as the contraction section, the test section inlet, and the wall surface near region. At the same time, 10-15 layers of expansion layers are set near the wall surface to accurately analyze the flow characteristics in the boundary layer. By adjusting the grid size and density, the number of grids is controlled between 12 million and 15 million, which balances the calculation efficiency while ensuring the calculation accuracy. Then, in the turbulent flow simulation setting, the large eddy simulation (LES) model is selected to analyze the turbulent flow. The large eddy simulation model can directly solve large-scale vortex motion and model small-scale vortices through a sub-grid model, which can more truly restore the turbulent fluctuation characteristics compared with the traditional Reynolds-averaged Navier-Stokes method. In the solving process, the filtered LES equation corresponding to the LES model is numerically solved to ensure that the equation satisfies the mass and momentum conservation laws on the discrete grid. Finally, the flow field information of the virtual position points in the virtual water tunnel equipment model is extracted by the computational fluid dynamics software, which can include fluid velocity, pressure, turbulence intensity and other information. Further, the collected flow field information is visualized. For example, the velocity distribution cloud map is used to intuitively display the acceleration and deceleration regions of the fluid in the virtual water tunnel equipment model, and the pressure distribution map is used to analyze the resistance distribution during fluid flow.

[0094] In step 2203, the flow field quality response information is determined according to the flow field information of the virtual position points by the flow field quality detection algorithm corresponding to the virtual test pipeline model.

[0095] The virtual position points can be pre-selected representative positions in the virtual test pipeline model, such as the position points of the center section of the virtual test pipeline model and the section 1 / 3 away from the inlet of the virtual test pipeline model. The flow field information of the virtual position points can include the instantaneous velocity value of the fluid at the virtual position points and the average velocity obtained through statistical analysis.

[0096] In some embodiments of the present application, the flow field quality response information includes a turbulence intensity and a velocity non-uniformity. The turbulence intensity is used to represent the intensity of fluctuation of the fluid velocity in the virtual fluid environment, i.e., the intensity of random fluctuation of the fluid in the average flow state. The higher the intensity, the stronger the disorder of the fluid motion. In the actual water tunnel test, too high turbulence intensity can affect the accuracy and stability of the test data. The velocity non-uniformity is used to represent the non-uniformity of the spatial distribution of the fluid velocity in the virtual fluid environment. The higher the velocity non-uniformity, the greater the difference in velocity at the cross section, which can cause vortex, separation and other adverse phenomena of fluid flow, affecting the test accuracy.

[0097] Therefore, the steady-state simulation of the virtual water tunnel equipment model in the virtual fluid environment can simulate the fluid flow under different working conditions in the computer environment. Compared with the real test, the virtual simulation can flexibly set the boundary conditions and initial conditions, quickly repeat the test, and greatly improve the verification efficiency of the design scheme. Moreover, the flow field quality response information obtained by processing the flow field information of the virtual position point by using the flow field quality detection algorithm corresponding to the virtual test pipe model enables the influence of different candidate structure 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 structure feature group can be accurately judged, which helps to improve the design efficiency of the water tunnel pipe information.

[0098] 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 can specifically include a turbulence intensity detection algorithm and a velocity non-uniformity detection algorithm. Figure 4 As shown in FIG. 22, the step 2203 can specifically include a step 22031 and a step 22032.

[0099] The step 22031 determines the turbulence intensity according to the flow field information of the virtual position point by using the turbulence intensity detection algorithm corresponding to the virtual test pipe model. The turbulence intensity is used to represent the intensity of fluctuation of the fluid velocity in the virtual fluid environment.

[0100] The flow field information of the virtual position point can include velocity information of the virtual position point at different time points in a preset time window.

[0101] The turbulence intensity detection algorithm can be represented by the following formula (1):

[0102] (1)

[0103] In the formula (1), represents the turbulence intensity, u v w ​​​​​​​​​​respectively, are the fluctuation velocities of the fluid in three coordinate axis directions of the virtual position point, is the time-averaged velocity.

[0104] It is worth mentioning here that the fluctuation 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 a stable velocity value obtained by averaging the fluid velocity within a preset time window, which is the reference parameter for calculating the turbulence intensity.

[0105] Exemplarily, for each virtual position point, according to the instantaneous velocity values of the virtual position point in three coordinate axis directions within a preset time window , , , then, the instantaneous velocities in each coordinate axis direction are time-averaged respectively to obtain the average velocities in each coordinate axis direction , , . Subsequently, the fluctuation velocities in each coordinate axis direction are obtained respectively according to the formula . u ', v ', w '. Finally, the turbulence intensity of each virtual position point is obtained through the above formula (1).

[0106] In this way, through the turbulence intensity detection algorithm, the original velocity data obtained by simulation can be converted into a quantitative turbulence intensity index, which intuitively reflects the fluctuation characteristics of the fluid at different virtual position points, and provides a basis for evaluating the turbulence suppression effect of the candidate structure feature group. For example, if the turbulence intensity corresponding to a certain candidate structure feature group is low, it indicates that the design of the candidate structure feature group helps to reduce the disorderly fluctuation of the fluid in the water tunnel equipment, improves the flow field stability of the water tunnel equipment, and optimizes the flow field quality of the test pipeline.

[0107] In step 22032, the velocity non-uniformity is determined according to the flow field information of the virtual position point through the velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model; the velocity non-uniformity is used to represent the non-uniformity of the spatial distribution of the fluid velocity in the virtual fluid environment of the virtual test pipeline model.

[0108] The flow field information of the virtual position point can further include the velocity information of the virtual position point within a preset cross section of the virtual test pipeline model. The velocity information of the virtual position point within the preset cross section can include the velocity information of the virtual position point of the center cross section of the virtual test pipeline model, the velocity information of the virtual position point of the 1 / 3 cross section from the inlet of the virtual test pipeline model.

[0109] The velocity non-uniformity detection algorithm can be represented by the following formula (2):

[0110] (2)

[0111] wherein, represents the velocity non-uniformity, U represents the average velocity of the preset cross section, n represents the number of virtual position points in the preset cross section, u i represents the time-averaged velocity of the i-th virtual position point in the preset cross section.

[0112] Exemplarily, first, the time-averaged velocity of each virtual position point in the n virtual position points in the preset cross section is extracted u i ; second, the average velocity U of the preset cross section is determined according to the time-averaged velocities of the n virtual position points u i ; finally, the velocity non-uniformity of the preset cross section is obtained by using the above formula (2), and the velocity non-uniformity value of the preset cross section is determined as the above velocity non-uniformity.

[0113] In this way, the velocity non-uniformity is calculated by the velocity non-uniformity detection algorithm, and the influence of different candidate structure feature groups on the uniformity of the velocity distribution in the test pipeline can be quantitatively evaluated. For example, if the velocity non-uniformity corresponding to a certain candidate structure feature group is small, it indicates that a type of pipeline designed based on the candidate structure feature group can effectively promote the uniform flow of fluid in the water tunnel equipment, reduce local flow rate abnormalities, and optimize the flow field quality of the test pipeline.

[0114] Therefore, by calculating the turbulence intensity and the velocity non-uniformity, the flow state of the fluid in the virtual test pipeline model can be quantified. The turbulence intensity directly reflects the intensity of the fluid velocity fluctuation, and the velocity non-uniformity clearly reflects the dispersion degree of the spatial distribution of the fluid velocity. The turbulence intensity and the velocity non-uniformity provide an explicit and objective basis for evaluating the influence of different candidate structure feature groups on the flow field quality of the test pipeline, avoiding the uncertainty of subjective judgment. Moreover, according to the calculated turbulence intensity and velocity non-uniformity, different candidate structure feature groups can be compared and analyzed. If the turbulence intensity and the velocity non-uniformity corresponding to a certain candidate structure feature group are both low, it indicates that this structure feature group is beneficial to improving the flow field quality and can be used as a reference for subsequent optimization design of the water tunnel equipment. In this way, the feature quantities of the candidate structure features can be continuously adjusted, and the simulation calculation and flow field quality evaluation can be performed again to gradually find the optimal design scheme, thereby improving the accuracy and reliability of the water tunnel test.

[0115] Furthermore, the target structure feature can be the M type candidate structure feature that has a key influence on the flow field quality of the test pipeline after being screened by the sensitivity value, according to step 230.

[0116] In some embodiments of the present application, in order to accurately screen M type target structure features from N type candidate structure features, as shown in formula (3), the sensitivity value of each candidate structure feature is calculatedFigure 5 As shown, the step 230 can specifically include a step 2301 and a step 2302.

[0117] The step 2301 determines a sensitivity value corresponding to each candidate structural feature according to the 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 represent the influence degree of the candidate structural feature on the flow field quality of the test pipeline.

[0118] The sensitivity value is used to quantitatively evaluate the numerical index of the influence degree of each candidate structural feature on the flow field quality of the test pipeline. The greater the sensitivity value is, the more significant the influence of the candidate structural feature on the flow field quality (such as the turbulence intensity and the velocity non-uniformity) of the test pipeline when changing the characteristic quantity of the candidate structural feature, that is, the higher the importance of the structural feature in the optimization design.

[0119] The step 2302 screens M target structural features from the N candidate structural features according to the sensitivity values corresponding to each candidate structural feature, and the sensitivity value of the target structural feature is greater than or equal to a preset sensitivity value.

[0120] The preset sensitivity value is a preset threshold value, which is used as a judgment standard. When the sensitivity value of a certain candidate structural feature is greater than or equal to the preset sensitivity value, it indicates that the candidate structural feature has a more significant influence on the flow field quality of the test pipeline, and is screened out as a target structural feature.

[0121] For example, the sensitivity values of each candidate structural feature calculated in the step 2301 are compared with the preset sensitivity value one by one, and M target structural features with sensitivity values greater than or equal to the preset sensitivity value are screened out from the N candidate structural features as target structural features.

[0122] Based on the above Table 1, the sensitivity (represented by P value, the greater the P value is, the higher the sensitivity is) of the turbulence intensity and the velocity non-uniformity two types of flow field quality on the six types of candidate structural features (X1-X6) in Table 1 is calculated, and the results are shown in Table 2.

[0123]

[0124] Table 2

[0125] Based on Table 2, for the turbulence intensity, the sensitivity ranking is: X2 > X1 > X3 > X4 > X5 > X6; for the velocity non-uniformity, the sensitivity ranking is: X3 > X1 > X2 > X6 > X5 > X4. Comprehensive analysis of the turbulence intensity and the velocity non-uniformity two flow field quality response information shows that the candidate structure characteristics X1, X2 and X3 all show high sensitivity in the two flow field quality response information, indicating that X1, X2 and X3 have a significant impact on the flow field quality of the test section. Therefore, the contraction section length X1, the contraction ratio X2 and the contraction curve type X3 are determined as the target structure characteristics. The candidate structure characteristics X4, X5 and X6 all show low sensitivity in the two flow field quality response information. Therefore, in the subsequent structure optimization process of the first type of pipeline, i.e., the contraction section, X4, X5 and X6 can be set as fixed values, for example, X4 is set as a square, X5 is set as a square, and X6 is set as a 4-face contraction.

[0126] Therefore, through the quantitative evaluation and screening of the sensitivity values, M target structure characteristics that have a significant impact on the flow field quality of the test pipeline can be accurately identified from N candidate structure characteristics. After the target structure characteristics are determined, the feature values of these key characteristics, i.e., the target structure characteristics, can be optimized, reducing the number of unnecessary tests and simulations, reducing the time cost and computational resource consumption in the test process, and thus improving the efficiency of determining the pipeline information of the water tunnel equipment.

[0127] In some embodiments of the present application, as shown in Figure 6 The step 2301 can specifically include a step 23011 and a step 23012.

[0128] In the step 23011, the relationship between the N candidate structure characteristics and the flow field quality response information is determined according to the flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups.

[0129] For example, the candidate structure characteristics (such as the contraction section length, the contraction ratio, the contraction curve type, the inlet shape, the outlet shape and the number of contraction faces) in each candidate structure characteristic group are taken as the independent variables, and the flow field quality response information (such as the turbulence intensity or the velocity non-uniformity) is taken as the dependent variable. By collecting the data points of the at least two candidate structure characteristic groups and the corresponding flow field quality response information, a polynomial function is fitted by using the least square method, so as to obtain a response surface model. The response surface model can intuitively show the relationship between the N candidate structure characteristics and the flow field quality response information. For example, the response surface model based on the turbulence intensity and the response surface model based on the velocity non-uniformity can be respectively constructed based on the foregoing method.

[0130] In the step 23012, the sensitivity value corresponding to each candidate structure characteristic is determined according to the relationship between the N candidate structure characteristics and the flow field quality response information.

[0131] Exemplarily, after determining the relationship between the N types of candidate structural features and the flow field quality response information, the sensitivity analysis of the candidate structural features can be performed in a manner of regression model, variance analysis, numerical simulation or experiment.

[0132] Taking the regression model as an example, the partial derivative of the above response surface model with respect to each type of candidate structural feature can be calculated, and the value of the partial derivative reflects the influence degree of the slight change of the type of candidate structural feature on the flow field quality response information, i.e. the sensitivity value, under the condition that other types of candidate structural features are unchanged. It can be understood that the greater the absolute value of the partial derivative, the greater the sensitivity value corresponding to the candidate structural feature.

[0133] Therefore, through the calculation and screening of the sensitivity value, the influence degree of each candidate structural feature on the flow field quality of the test pipeline can be quantified, so as to focus on the target structural features that have greater influence on the flow field quality, avoid excessive experiments on the candidate structural features that have less influence on the results, and more efficiently determine the water tunnel pipeline information of the water tunnel equipment.

[0134] Then, step 240 is involved, and the water tunnel pipeline information includes a target feature quantity corresponding to each type of target structural feature in the M types of target structural features. In some embodiments of the present application, as shown in Figure 7 the above step 240 can specifically include steps 2401 to 2406.

[0135] Step 2401: determining P first reference feature quantities corresponding to each type of target structural feature according to the feature quantity corresponding to each type of target structural feature in the M types of target structural features.

[0136] Exemplarily, P feature quantities can be selected as the first reference feature quantities from the feature quantity corresponding to each type of target structural feature by equidistant selection. For example, the M types of target structural features can include the length of the contraction section X1, the contraction ratio X2 and the contraction curve type X3, and the feature quantity corresponding to each type of target structural feature can refer to the above table 1, wherein the P first reference feature quantities corresponding to each type of target structural feature can 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), X3 (fifth power, Weierstrass, B-S).

[0137] Further, in some embodiments of the present application, before step 2401, the feature quantity corresponding to each type of target structural feature can be determined from the reference feature quantity corresponding to each type of target structural feature by the steepest ascent experiment.

[0138] Specifically, the steepest ascent experiment refers to determining the climbing direction by the response surface model determined through the above step 2301, wherein the coefficients in the response surface model are PBD (Plackett-Burman Design) regression equation coefficients, which represent the influence direction and degree of the corresponding candidate structural feature on the flow field quality response information. When the PBD regression equation coefficient is positive, it indicates that the candidate structural feature is positively correlated with the flow field quality response information, that is, when the characteristic quantity of the candidate structural feature increases, the value of the flow field quality response information also increases, which is a positive effect parameter. When the PBD regression equation coefficient is negative, it indicates that the candidate structural feature is negatively correlated with the flow field quality response information, that is, when the characteristic quantity of the candidate structural feature increases, the value of the flow field quality response information will decrease, which is a negative effect parameter. The absolute value of the PBD regression equation coefficient reflects the strength of the influence of the candidate structural feature, and the larger the absolute value, the more significant the influence of the candidate structural feature on the flow field quality response information, and in the steepest ascent experiment, the corresponding step size will also be larger. When performing the climbing experiment, the characteristic quantity of the candidate structural feature is adjusted step by step according to the set step size, and after each adjustment, the experiment is performed, the turbulence degree and velocity non-uniformity are measured, and the relative error with the target turbulence degree and target velocity non-uniformity is calculated. When the relative error reaches a minimum value and then starts to increase, the experiment is stopped at this moment, and the determination of the reference characteristic quantity of the candidate structural feature is completed. Subsequently, the reference characteristic quantity corresponding to the minimum error of the target structural feature in the steepest ascent experiment can be used as the center point of subsequent optimization, so as to determine the characteristic quantity corresponding to each type of target structural feature.

[0139] For example, through the aforementioned steepest ascent experiment, the significance and optimal interval of M types of target structural features (such as contraction section length, contraction ratio, contraction curve type) can be determined, and equal-interval selection can be performed near the optimal interval to obtain the characteristic quantity corresponding to each type of target structural feature in the M types of target structural features. For example, if the steepest ascent experiment determines that the contraction section length is in the optimal region of 1.4-2.8 m, when selecting the first reference characteristic quantity of X1 in step 2401, the selection can be focused on this range, such as 1.4 m, 1.6 m, 1.8 m, 2.0 m, etc., so that the selected characteristic quantity is more reasonable and representative.

[0140] Step 2402, randomly combine the P first reference characteristic 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.

[0141] Exemplarily, the first reference characteristic quantities corresponding to the M types of target structural features are combined in a full combination manner. Specifically, for each type of target structural feature, one first reference characteristic quantity is selected from the P first reference characteristic quantities corresponding to the type of target structural feature, and then the first reference characteristic quantities are combined together to form a first test structural feature group.

[0142] In step 2403, Q second reference characteristic quantities are selected from the P first reference characteristic quantities corresponding to each type of target structural feature according to the flow field quality information corresponding to each of the L first test structural feature groups.

[0143] Exemplarily, for each of the L first test structural feature groups, a corresponding virtual water tunnel equipment model is constructed, and a steady-state simulation is performed in a virtual fluid environment to obtain the flow field quality information of each virtual water tunnel equipment model. For details, reference can be made to the above steps 2201 to 2203, which will not be repeated here.

[0144] Subsequently, the Q second reference characteristic quantities with better flow field quality improvement effect can be determined from the P first reference characteristic quantities corresponding to each type of target structural feature according to the preset flow field quality information by comparing the flow field quality information when the same target structural feature takes different characteristic quantities in different first test structural feature groups. For example, if the preset turbulence section in the preset flow field quality information is set to 4.5% and the preset non-uniformity in the preset flow field quality information is set to 4.5%, the Q second reference characteristic quantities corresponding to each type of target structural feature can include X1 (1.4 m, 2.0 m, 2.8 m), X2 (1:3, 1:5, 1:9), and X3 (fifth power, Vickers, B-S), respectively.

[0145] In step 2404, the Q second reference characteristic quantities corresponding to each type of target structural feature in the M types of target structural features are combined by an orthogonal test design method to generate L second test structural feature groups.

[0146] Exemplarily, the orthogonal test design method is used to select a suitable orthogonal table according to the M types of target structural features and the Q second reference characteristic quantities corresponding to each type of target structural feature. The orthogonal table is a pre-designed table that can ensure that the combination of each test factor (target structural feature) and test level (second reference characteristic quantity) is fully investigated with fewer test times. Then, the M types of target structural features are assigned to the columns of the orthogonal table, and the Q second reference characteristic quantities of each type of target structural feature correspond to the levels in the orthogonal table. Each row of the orthogonal table corresponds to a parameter combination, thereby generating L second test structural feature groups.

[0147] Taking the M-type target structure characteristics including the contraction section length X1, the contraction ratio X2 and the contraction curve type X3 as an example, a three-factor and three-level orthogonal test can be set up, three levels are selected for each factor, L9(3 4 ) is selected as the orthogonal table of the orthogonal test according to the selection principle of the orthogonal table, and the orthogonal table of the orthogonal test can be obtained according to the three types of target structure characteristics and the characteristic quantities corresponding to the three levels of each type of target structure characteristic, as shown in Table 3.

[0148]

[0149] Table 3

[0150] In step 2405, the target structure characteristic group is screened from the L second test structure characteristic groups according to the flow field quality response information corresponding to each second test structure characteristic group in the L second test structure characteristic groups.

[0151] Then, the methods of steps 2201 to 2203 are used to determine (a)~(i) the turbulence intensity and velocity non-uniformity corresponding to each second test structure characteristic group in the nine second test structure characteristic groups.

[0152] In one example, the target structure characteristic group can include a first target structure characteristic group screened based on the turbulence intensity and a second target structure characteristic group screened based on the velocity non-uniformity, based on which the first target structure characteristic group can be screened from the L second test structure characteristic groups according to the turbulence intensity, and the second target structure characteristic group can be screened from the L second test structure characteristic groups according to the velocity non-uniformity.

[0153] Next, step 2405 will be specifically described in combination with the orthogonal table of the orthogonal test shown in Table 3.

[0154] Firstly, based on the orthogonal table shown in Table 3, nine second test structure characteristic groups are constructed, and the contraction section structures corresponding to the nine second test structure characteristic groups are shown in (a)~(i) of FIG. 1. Figure 8

[0155] To further analyze the influence of the contraction section structures constructed by the nine second test structure characteristic groups on the flow field quality of the test section of the contraction section structure, the turbulence intensity of the central axis of the water tunnel pipeline corresponding to each contraction section structure is calculated by the foregoing step 220, and a turbulence section change trend graph is constructed as shown in FIG. 2. Figure 9 Figure 9 The serial numbers (1)~(9) in FIG. 2 form a one-to-one correspondence with (a)~(i) of FIG. 1. Figure 8

[0156] Referring to FIG. 3, the turbulence intensity of the central axis of the water tunnel pipeline corresponding to each contraction section structure is calculated by the foregoing step 220, and a turbulence section change trend graph is constructed as shown in FIG. 3. Figure 9 ​​​It can be seen that the contraction section structure corresponding to the serial number (8) exhibits the lowest turbulence intensity 6.4% at the test section, which corresponds to the (h) subgraph, and the corresponding second test structure feature group is: the contraction ratio X2 is 1:9, the contraction curve type X3 is Vickers, and the contraction section length X1 is 1.4 m.

[0157] Figure 8 The turbulence intensity values at 1 / 3 of the inflow direction of the test section affected by each contraction section structure are shown in Table (4).

[0158]

[0159] Table 4

[0160] Then, based on the above Table 4, the analysis of the 9 second test structure feature groups is carried out to screen the first target structure feature group from the L second test structure feature groups based on the turbulence intensity:

[0161] For the contraction ratio X2, the sum of the turbulence intensities corresponding to all second test structure feature groups (1, 2, 3) with a contraction ratio of 1:3 is recorded as K1 of X2; the sum of the turbulence intensities corresponding to all second test structure feature groups (4, 5, 6) with a contraction ratio of 1:5 is recorded as K2 of X2; the sum of the turbulence intensities corresponding to all second test structure feature groups (7, 8, 9) with a contraction ratio of 1:9 is 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, 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.

[0162] For the contraction curve type X3, the turbulent flow degrees corresponding to all the second test structural feature groups (1, 4, 7) with the contraction curve type of the fifth power are added and recorded as K1 of X3; the turbulent flow degrees corresponding to all the second test structural feature groups (2, 5, 8) with the contraction curve type of the von Karman are added and recorded as K2 of X3; the turbulent flow degrees corresponding to all the second test structural feature groups (3, 6, 9) with the contraction curve type of B-S are added and 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, the average value of K1 of X3 is calculated = 0.093; the average value of K2 of X3 is calculated = 0.0827; the average value of K3 of X3 is calculated = 0.079; then, the range R of X3 is calculated: R of X3 = 0.093 - 0.079 = 0.014.

[0163] For the contraction section length X1, the turbulent flow degrees corresponding to all the second test structural feature groups (1, 6, 8) with the contraction section length of 1.4 m are added and recorded as K1 of X1; the turbulent flow degrees corresponding to all the second test structural feature groups (2, 4, 9) with the contraction section length of 2.1 m are added and recorded as K2 of X1; the turbulent flow degrees corresponding to all the second test structural feature groups (3, 5, 7) with the contraction section length of 2.8 m are 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, the average value of K1 of X1 is calculated = 0.0847; the average value of K2 of X1 is calculated = 0.085; the average value of K3 of X1 is calculated = 0.085; then, the range R of X1 is calculated: R of X1 = 0.085 - 0.0847 = 0.0003.

[0164] Since, the greater the range of the target structural feature, the more important the target structural feature is. Based on the range R of each target structural feature, the contraction ratio X2, the contraction curve type X3 and the contraction section length X1 are analyzed and sorted, and the result is: contraction ratio X2 > contraction curve type X3 > contraction section length X1.

[0165] To visually represent, the second reference feature quantity of each target structural feature is taken as the horizontal coordinate, and the average value of the turbulent flow degree corresponding to the second reference feature quantity of the target structural feature is taken as the vertical coordinate, and a graph is constructed as Figure 10The graph shown is used to represent the relationship between the second reference characteristic quantity and the turbulence degree.

[0166] As can be seen from Figure 10 , with the turbulence degree as the screening basis, the value of the second reference characteristic quantity of the contraction ratio is 1:9, the value of the second reference characteristic quantity of the contraction curve type is B-S, and the value of the second reference characteristic quantity of the contraction section length is 1.4 m, which is the first target structure characteristic group. Based on this, the turbulence degree corresponding to the first target structure characteristic group is calculated to be 4.6%, and thus the turbulence section of the test section affected by the contraction section is reduced by 28%.

[0167] Similarly, based on the above optimization analysis, the second target structure characteristic group is screened from the L second test structure characteristic groups based on the turbulence degree, and a graph shown in Figure 11 for representing the relationship between the second reference characteristic quantity and the velocity non-uniformity can be obtained. As can be seen from Figure 11 , with the velocity non-uniformity as the screening basis, the value of the second reference characteristic quantity of the contraction ratio is 1:5, the value of the second reference characteristic quantity of the contraction curve type is Vickers, and the value of the second reference characteristic quantity of the contraction section length is 2 m, which is the second target structure characteristic group. Based on this, the velocity non-uniformity corresponding to the first target structure characteristic group is calculated to be 1.1%, and thus the velocity non-uniformity of the test section affected by the contraction section is reduced by 42%.

[0168] The contraction section structure based on the above first target characteristic group can refer to subgraph (a) in Figure 12 , and the contraction section structure based on the above second target characteristic group can refer to subgraph (b) in Figure 12 .

[0169] In another example, a multi-objective genetic algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II) can be combined to screen a Pareto front solution set from the orthogonal test results with the minimum turbulence degree and velocity non-uniformity as the target, and then determine the target structure characteristic group based on the Pareto front solution set.

[0170] In step 2406, the water tunnel pipeline information of a type of pipeline is determined according to the target structure characteristic group.

[0171] Therefore, by gradually screening and optimizing the characteristic quantities of the M target structure characteristics, the optimal design scheme, i.e., the target structure characteristic group that has the greatest impact on the flow field quality, can be quickly found from a large number of possible structure characteristic groups, greatly reducing the number of tests and calculation amount, and improving the determination efficiency of the water tunnel pipeline information.

[0172] In some 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-type target structure feature group. Based on this, before the step 2406, the method for determining the contraction section structure information of the water tunnel device can further include: obtaining measured flow field quality response information of a water tunnel device entity model, the water tunnel device entity model being constructed based on the target structure feature group.

[0173] The water tunnel device entity model is a scaled-down test model constructed based on the target structure feature group according to a predetermined scale and water tunnel device structure requirements, for simulating the actual working conditions of the water tunnel device. The scaled-down test model is a model scaled down by a certain ratio from the actual water tunnel device, to reduce the test cost and complexity, while ensuring that the water tunnel device entity model and the actual water tunnel device have similarity in fluid dynamics characteristics.

[0174] For example, obtaining the measured flow field quality response information of the water tunnel device entity model can specifically include:

[0175] The particle image velocimetry (PIV) is used to obtain the measured flow field quality response information of the water tunnel device entity model. The particle image velocimetry is a non-contact fluid velocity measurement method. By scattering tracer particles into the flow field, illuminating the particles with laser, and then shooting particle images with a high-speed camera, the velocity distribution of the flow field and other information can be calculated according to the position changes of the particles at different times, and the flow field of the test pipeline is measured, and finally the measured flow field quality response information of the water tunnel device entity model is obtained. The measured flow field quality response information can reflect various quality characteristics of the flow field of the water tunnel device entity model in actual operation, such as turbulence intensity and velocity non-uniformity.

[0176] Based on this, the step 2406 can specifically include: in the case that 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 predetermined difference, determining 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.

[0177] The predetermined difference is an allowable error range set in advance according to actual engineering requirements and accuracy requirements, for judging the degree of conformity between the measured flow field quality response information and the expected flow field quality response information.

[0178] In the design and optimization process of the water tunnel device, the result obtained by simulating the virtual test pipe model, i.e., the flow field quality response information corresponding to the target structure feature group, is compared and verified with the actually measured result, i.e., the actually measured flow field quality response information. When the difference between the two is less than or equal to the preset difference, it means that the simulation result based on the virtual test pipe model is close to the actual situation. In this way, it is indicated that the flow field quality presented by the target structure feature group obtained through the foregoing series of screening steps is consistent with the actually measured flow field quality. This consistency can enhance the reliability of the simulation result and ensure that the design and optimization based on the virtual model have practical application value.

[0179] In some non-limiting embodiments of the present application, in the case where the difference between the actually measured flow field quality response information and the flow field quality response information corresponding to the target structure feature group is greater than the preset difference, the foregoing steps 210 to 240 are iteratively performed until the difference between the actually 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, to obtain the water tunnel pipe information of a type of pipe.

[0180] Therefore, by constructing the water tunnel device entity model and measuring the flow field quality information of the test pipe of the water tunnel device entity model using the particle image velocimetry technology, the flow field quality of the water tunnel pipe entity model can be accurately measured, and by comparing with the flow field quality response information corresponding to the target structure feature group, the reliability of the design method can be verified, i.e., whether the water tunnel pipe designed based on the target structure feature group can achieve the expected flow field quality requirement in actual operation can be judged, to provide a reliable basis for the design and optimization and actual application of the water tunnel device.

[0181] Based on the water tunnel device contraction section structure information determination method provided in the foregoing embodiments, the present application also provides a specific implementation mode of a water tunnel device contraction section structure information determination apparatus. Please refer to the following embodiments.

[0182] Referring to Figure 13 The water tunnel device contraction section structure information determination apparatus 300 provided in the embodiments of the present application comprises:

[0183] The first acquisition module 310 is configured to acquire at least two candidate structure feature groups of a type of pipe, each candidate structure feature group comprising N types of candidate structure features and a feature quantity corresponding to each type of candidate structure feature, and N is greater than or equal to 3;

[0184] The first determination module 320 is configured to determine, according to the at least two candidate structure feature groups, flow field quality response information corresponding to each candidate structure feature group in the at least two candidate structure feature groups; wherein the flow field quality response information is used to reflect the degree of excellence or inferiority of the flow field quality of a test pipe affected by a type of pipe constructed by the candidate structure feature group;

[0185] The screening module 330 is configured to screen M target structural features from the N candidate structural features according to the flow field quality response information corresponding to each of the at least two candidate structural feature groups;

[0186] The second determination module 340 is configured to determine the water tunnel pipeline information of the pipeline according to the M target structural features, the water tunnel pipeline information including information for characterizing the structure of the pipeline.

[0187] Thus, the flow field quality response information corresponding to each of the at least two candidate structural feature groups determined by the first determination module 320 based on the first acquisition module 310 can reflect the influence of different structural feature groups on the flow field quality of the test pipeline. Subsequently, the screening module 330 can screen M target structural features from the N candidate structural features based on the comprehensive evaluation of the flow field quality response information of the multiple candidate structural feature groups, so as to accurately screen multiple structural features that have a greater influence on the flow field quality of the water tunnel equipment. Compared with the one-by-one checking manner of the single factor design method, this method considers the synergistic effect of multiple structural features, can quickly determine the main structural features that affect the flow field quality of the test pipeline, avoids multiple ineffective tests on secondary structural features, and effectively improves the determination efficiency of the water tunnel pipeline information. The second determination module 340 determines the water tunnel pipeline information of the pipeline based on the accurately screened multiple target structural features, which can more efficiently and comprehensively optimize the performance of the water tunnel equipment, and thus accelerate the research process of the hydrodynamic characteristics of the underwater vehicle.

[0188] The various modules of the water tunnel equipment contraction section structure information determination apparatus 300 provided by the embodiments of the present application can realize Figure 1 The various steps of the water tunnel equipment contraction section structure information determination method provided by the embodiments of the present application can realize the functions of the steps and achieve the corresponding technical effects. For brevity, the details are not described here.

[0189] In some embodiments of the present application, the first determination module 320 in the embodiments of the present application can specifically include:

[0190] The construction sub-module is configured to construct a virtual water tunnel equipment model corresponding to each of the at least two candidate structural feature groups, the virtual water tunnel equipment model including a virtual pipeline model and a virtual test pipeline model;

[0191] The first determination sub-module is configured to perform 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, the flow field information being information for characterizing the flow state of the fluid flowing through the virtual test pipeline model at the virtual position point;

[0192] The second determining sub-module is configured to determine flow field quality response information according to the flow field information of the virtual position point by using a flow field quality detection algorithm corresponding to the virtual test pipeline model.

[0193] In some embodiments of the present application, the second determining sub-module in the embodiments of the present application can be specifically configured to:

[0194] In a case where the flow field quality response information includes a turbulence intensity and a velocity non-uniformity, and the flow field quality detection algorithm includes a turbulence intensity detection algorithm and a velocity non-uniformity detection algorithm, the turbulence intensity is determined according to the flow field information of the virtual position point by using a turbulence intensity detection algorithm corresponding to the virtual test pipeline model; the turbulence intensity is used to represent the intensity of the fluctuation of the fluid velocity in the virtual fluid environment of the virtual test pipeline model.

[0195] The velocity non-uniformity is determined according to the flow field information of the virtual position point by using a velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model; the velocity non-uniformity is used to represent the non-uniformity of the spatial distribution of the fluid velocity in the virtual fluid environment of the virtual test pipeline model.

[0196] In some embodiments of the present application, the screening module 330 in the embodiments of the present application can specifically include:

[0197] The third determining sub-module is configured to determine a sensitivity value corresponding to each candidate structural feature according to the 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 represent the influence degree of the candidate structural feature on the flow field quality of the test pipeline.

[0198] The first screening sub-module is configured to screen M target structural features from the N candidate structural features according to the sensitivity value corresponding to each candidate structural feature; the sensitivity value of the target structural feature is greater than or equal to a preset sensitivity value.

[0199] In some embodiments of the present application, the third determining sub-module in the embodiments of the present application can be specifically configured to:

[0200] determine a relationship between the N candidate structural features and the flow field quality response information according to the flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups;

[0201] determine the sensitivity value corresponding to each candidate structural feature according to the relationship between the N candidate structural features and the flow field quality response information.

[0202] In some embodiments of the present application, the second determining module 340 in the embodiments of the present application can specifically include:

[0203] a fourth determining sub-module, configured to, in a case where the water tunnel pipeline information comprises target feature quantities corresponding to each of M types of target structure features, determine P first reference feature quantities corresponding to each of the M types of target structure features according to the target feature quantities corresponding to each of the M types of target structure features;

[0204] a first combining sub-module, configured to randomly combine the P first reference feature quantities corresponding to each of the M types of target structure features to obtain L first test structure feature groups;

[0205] a second screening sub-module, configured to screen Q second reference feature quantities from the P first reference feature quantities corresponding to each of the M types of target structure features according to flow field quality information corresponding to each of the L first test structure feature groups;

[0206] a second combining sub-module, configured to combine the Q second reference feature quantities corresponding to each of the M types of target structure features by an orthogonal test design method to generate L second test structure feature groups;

[0207] a third screening sub-module, configured to screen a target structure feature group from the L second test structure feature groups according to flow field quality response information corresponding to each of the L second test structure feature groups;

[0208] a fifth determining sub-module, configured to determine the water tunnel pipeline information of the type of pipeline according to the target structure feature group.

[0209] In some embodiments of the present application, the contraction section structure information determination apparatus of the water tunnel equipment in the embodiments of the present application can further comprise:

[0210] a second obtaining module, configured to, before the water tunnel pipeline information comprises target feature quantities corresponding to each of M types of target structure features and the water tunnel pipeline information of the type of pipeline is determined according to the target structure feature group, obtain measured flow field quality response information of a water tunnel equipment entity model, the water tunnel equipment entity model being constructed based on the target structure feature group.

[0211] The second determining module 340 can be configured to, in a case where a 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, determine the second reference feature quantities corresponding to each of the target structure features in the target structure feature group as the target feature quantities corresponding to each of the target structure features.

[0212] Figure 14 A hardware structure schematic diagram of an electronic device provided by some embodiments of the present application is shown.

[0213] The electronic device can include a processor 401 and a memory 402 having stored thereon computer program instructions.

[0214] In particular, the processor 401 described above can include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits that embody the embodiments of the present application.

[0215] The memory 402 can include a mass storage that is used for data or instructions. By way of example, and not limitation, the memory 402 can include a hard disk drive (HDD), a floppy disk drive, a 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. The memory 402 can include removable or non-removable (or fixed) media, where appropriate. The memory 402 can be internal or external to the integrated gateway disaster recovery device, where appropriate. In particular embodiments, the memory 402 is non-volatile, solid-state memory.

[0216] In particular embodiments, the memory 402 can include read-only memory (ROM), random access memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, in general, the memory 402 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to perform operations described with reference to the data processing method according to the first aspect of the present application.

[0217] The processor 401 implements the structure information determination method of the contraction section of the water tunnel device according to any one of the above embodiments by reading and executing the computer program instructions stored in the memory 402.

[0218] In one example, the electronic device can further include a communication interface 403 and a bus 410. As shown, the processor 401, the memory 402, and the communication interface 403 are connected through the bus 410 and complete communication with each other. Figure 9

[0219] The communication interface 403 is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the present application.

[0220] ​Bus 410 includes a hardware, software, or both that couples components of electronic device to each other. As an example and not by way of limitation, bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) 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 another suitable bus or a combination of two or more of these. Where appropriate, bus 410 can include one or more buses. Although this application describes and shows a particular bus, this application contemplates any suitable bus or interconnect.

[0221] The electronic device can perform the contraction section structure information determination method of the water tunnel device in the embodiments of the application, thereby realizing the contraction section structure information determination method of the water tunnel device in combination with Figures 2 to 8 The contraction section structure information determination method and device of the water tunnel device described.

[0222] In addition, in combination with the contraction section structure information determination method of the water tunnel device in the above embodiments, the embodiments of the application can provide a computer readable storage medium to realize. The computer readable storage medium has computer program instructions stored thereon; the computer program instructions are executed by the processor to realize the contraction section structure information determination method of the water tunnel device in any one of the above embodiments. Examples of the computer readable storage medium include non-transitory computer readable storage medium, such as portable disc, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), portable compact disc read only memory (CD-ROM), optical storage device, magnetic storage device, etc.

[0223] In addition, in combination with the contraction section structure information determination method of the water tunnel device in the above embodiments, the embodiments of the application can provide a computer program product to realize. The program product is stored in a storage medium, and specifically can include a computer program or instructions, and the computer program or instructions are executed by the processor to realize the contraction section structure information determination method of the water tunnel device in any one of the above embodiments. The program product is executed by at least one processor to realize each process of the contraction section structure information determination method of the water tunnel device as described above, and can achieve the same technical effects, to avoid repetition, which will not be described here.

[0224] It is to be understood that the application is not limited to particular configurations and processes described herein and shown in the drawings. The detailed description is not to be taken as limiting the application. In the above embodiments, several specific steps are described and illustrated in order to provide a thorough understanding of the application. However, the application can be practiced with fewer or additional steps, and in a different order. The application is to be limited only by the claims.

[0225] The functions noted in the structural block diagrams above can be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, for example, they can be an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium, or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, Intranet, etc.

[0226] It is also to be understood that the example embodiments described herein are based on a series of steps or apparatuses to describe some methods or systems. However, the application is not limited to the order of the steps described above, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0227] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. Such processes can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These processes can be embodied in machine-executable code, which can be used to program computers or other programmable data processing apparatus. The processes can also be stored into one or more computer-readable storage media (memory or mass storage) that can direct computers or other programmable data processing devices to function in a particular manner, such that the processes of the computer-readable storage media (memory or mass storage) induce the computer or other programmable data processing apparatus to provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0228] The above is merely specific implementation of the present application, and 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 foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed in the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method of determining the configuration information of a contraction section of a water tunnel apparatus, characterized by, The water tunnel device comprises a first pipe and a test pipe associated with the first pipe, the structure of the first pipe affects the flow field quality of the test pipe, the first pipe is a contraction section, and the test pipe is a test section; the method comprises: obtaining at least two candidate structure characteristic groups of the first pipe, each candidate structure characteristic group comprising N types of candidate structure characteristics and a characteristic quantity corresponding to each type of candidate structure characteristic, N being greater than or equal to 3; determining flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups according to the at least two candidate structure characteristic groups; wherein the flow field quality response information is used to reflect the degree of advantage or disadvantage of the flow field quality of the test pipe affected by the first pipe constructed by the candidate structure characteristic group; screening M types of target structure characteristics from the N types of candidate structure characteristics according to the flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups, M being greater than or equal to 2; determining water tunnel pipe information of the first pipe according to the M types of target structure characteristics, the water tunnel pipe information comprising information used to represent the structure of the first pipe; wherein the flow field quality response information comprises a turbulent intensity and a velocity non-uniformity, and the determination of the flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups according to the at least two candidate structure characteristic groups comprises: constructing a virtual water tunnel device model corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups, the virtual water tunnel device model comprising a first virtual pipe model and a virtual test pipe model; performing steady-state simulation on the virtual water tunnel device model through a virtual fluid environment to obtain flow field information of a virtual position point of the virtual test pipe model, the flow field information being used to represent information of a flow state of fluid flowing through the virtual test pipe model at the virtual position point; determining the turbulent intensity according to the flow field information of the virtual position point through a turbulent intensity detection algorithm corresponding to the virtual test pipe model, the turbulent intensity being used to represent the intensity of fluctuation of fluid velocity in the virtual test pipe model in the virtual fluid environment; determining the velocity non-uniformity according to the flow field information of the virtual position point through a velocity non-uniformity detection algorithm corresponding to the virtual test pipe model, the velocity non-uniformity being used to represent the non-uniformity degree of spatial distribution of fluid velocity in the virtual test pipe model in the virtual fluid environment.

2. The method of claim 1, wherein, The screening of the M types of target structure characteristics from the N types of candidate structure characteristics according to the flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups comprises: determining a sensitivity value corresponding to each candidate structure characteristic according to the flow field quality response information corresponding to each candidate structure characteristic group in the at least two candidate structure characteristic groups; the sensitivity value is used to represent the influence degree of the candidate structure characteristic on the flow field quality of the test pipe. Screening M target structural features from the N candidate structural features according to the sensitivity values corresponding to each of the candidate structural features, the sensitivity value of the target structural feature being greater than or equal to a preset sensitivity value.

3. The method of claim 2, wherein, The method further comprises: According to the flow field quality response information corresponding to each of the at least two candidate structural feature groups, determining the sensitivity value corresponding to each of the candidate structural features. According to the flow field quality response information corresponding to each of the at least two candidate structural feature groups, determining the relationship between the N candidate structural features and the flow field quality response information.

4. The method of claim 1, wherein, According to the relationship between the N candidate structural features and the flow field quality response information, determining the sensitivity value corresponding to each of the candidate structural features. The water tunnel pipeline information includes a target feature quantity corresponding to each of the M target structural features; and the method further comprises: According to the feature quantity corresponding to each of the M target structural features, determining P first reference feature quantities corresponding to each of the target structural features; Randomly combining the P first reference feature quantities corresponding to each of the M target structural features to obtain L first test structural feature groups; According to the flow field quality information corresponding to each of the L first test structural feature groups, screening Q second reference feature quantities from the P first reference feature quantities corresponding to each of the target structural features; Combining the Q second reference feature quantities corresponding to each of the M target structural features by an orthogonal test design method to generate 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, screening a target structural feature group from the L second test structural feature groups; 5. The method of claim 4, wherein, According to the target structural feature group, determining the water tunnel pipeline information of the pipeline. The water tunnel pipeline information includes a target feature quantity corresponding to each of the M target structural features; Before the step of determining the water tunnel pipeline information of the pipeline according to the target structural feature group, the method further comprises: Obtaining measured flow field quality response information of a water tunnel equipment entity model, the water tunnel equipment entity model being constructed based on the target structural feature group; The step of determining the water tunnel pipeline information of the pipeline according to the target structural feature group comprises:

6. A device for determining the structural information of the contraction section of a water tunnel device, characterized in that, In a case where a 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 a preset difference, determining the second reference feature quantity corresponding to each of the target structural features in the target structural feature group as the target feature quantity corresponding to each of the target structural features. A water tunnel equipment includes a pipeline and a test pipeline associated with the pipeline, a structure of the pipeline affects a flow field quality of the test pipeline, the pipeline is a contraction section, and the test pipeline is a test section; and the device comprises: The first obtaining module is configured to obtain at least two candidate structural feature groups of the type of pipeline, each candidate structural feature group comprising N types of candidate structural features and feature quantities corresponding to each type of candidate structural feature, N being greater than or equal to 3; The first determining module is configured to determine, according to the at least two candidate structural feature groups, flow field quality response information corresponding to each candidate structural feature group in the at least two candidate structural feature groups; wherein the flow field quality response information is used to reflect the degree of advantage or disadvantage of the flow field quality of the test pipeline affected by the candidate structural feature group; The screening module is configured to screen 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, M being greater than or equal to 2; The second determining module is configured to determine water tunnel pipeline information of the type of pipeline according to the M types of target structural features, the water tunnel pipeline information comprising information used to represent the structure of the type of pipeline; The flow field quality response information comprises a turbulent intensity and a velocity non-uniformity, and the first determining module is specifically configured to: construct a virtual water tunnel equipment model corresponding to each candidate structural feature group in the at least two candidate structural feature groups, the virtual water tunnel equipment model comprising a type of virtual pipeline model and a virtual test pipeline model; perform 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, the flow field information being used to represent information of a flow state of fluid flowing through the virtual test pipeline model at the virtual position point; determine the turbulent intensity according to the flow field information of the virtual position point through a turbulent intensity detection algorithm corresponding to the virtual test pipeline model, the turbulent intensity being used to represent an intensity of fluctuation of fluid velocity of the virtual test pipeline model in the virtual fluid environment; and determine the velocity non-uniformity according to the flow field information of the virtual position point through a velocity non-uniformity detection algorithm corresponding to the virtual test pipeline model, the velocity non-uniformity being used to represent a non-uniform degree of spatial distribution of fluid velocity of the virtual test pipeline model in the virtual fluid environment.

7. An electronic device, comprising: The device comprises a processor and a memory storing computer program instructions; The processor executes the computer program instructions to implement the method for determining contraction section structural information of a water tunnel equipment according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer program instructions are stored on the computer readable storage medium and are executed by the processor to implement the method for determining contraction section structural information of a water tunnel equipment according to any one of claims 1-5.

9. A computer program product, characterised in that, The instructions in the computer program product are executed by the processor of the electronic device to enable the electronic device to perform the method for determining contraction section structural information of a water tunnel equipment according to any one of claims 1-5.

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