A design and evaluation method for a high-performance wind tunnel visualization nozzle

Abstract

A design and evaluation method for a hypersonic wind tunnel visualization nozzle involves the field of hypersonic wind tunnel technology. It addresses the problem that traditional flow field performance evaluation methods have difficulty capturing detailed defects in the three-dimensional flow field. First, the nozzle profile is designed based on the improved Sivells method, and the axial Mach number distribution is constructed in combination with transonic theory. Second, the glass material is thermoplasticized using high-temperature flame spray technology to achieve precise control of the nozzle curvature. Subsequently, tracer particle images are collected synchronously using a dual-pulse laser and a high-speed camera, and the three-dimensional velocity field is reconstructed and key flow field parameters are extracted using a cross-correlation algorithm. Finally, numerical simulation and genetic algorithms are combined to optimize the nozzle profile parameters, and iterative corrections are made until the flow field uniformity meets the standard. This method breaks through the spatial limitations of traditional probe measurements and realizes the dynamic capture of the full-domain flow field. At the same time, by quantitatively correlating geometric errors and flow field distortion, the reliability of wind tunnel test data and the efficiency of design optimization are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hypersonic wind tunnels, and in particular to a design and evaluation method for a hypersonic wind tunnel visualization nozzle. Background Art

[0002] In the defense science and technology industry, wind tunnel testing serves as a "ground-based benchmark" for verifying aerodynamic characteristics in major projects, such as hypersonic vehicles and next-generation aircraft engines. The reliability of its data directly determines the engineering accuracy of equipment performance. As a core component of high-precision wind tunnels, the performance of laminar nozzles directly determines the stability and uniformity of the airflow in the test section, which in turn affects the reliability of aerodynamic and thermal characteristic data. However, with the increasing demand for complex flow simulation in new equipment models, traditional flow field performance evaluation methods have exposed problems such as difficulty in capturing detailed defects in three-dimensional flow fields, large blind spots in flow field quality assessment, poor quantitative correlation between surface geometry errors and flow field distortion, and low confidence in boundary layer transition test data. There is an urgent need to establish a precise wind tunnel flow field evaluation system through breakthroughs in new flow field diagnostic technologies. Summary of the Invention

[0003] To address the problem that traditional flow field performance evaluation methods are unable to capture the details of three-dimensional flow fields, the present invention provides a design and evaluation method for a high-performance wind tunnel visualization nozzle, comprising:

[0004] S1. Nozzle Design: An improved Sivells method is used to construct an axial Mach number distribution function to calculate the wall profile. Incorporating transonic theory, a relationship between nozzle length and profile curvature is established. Computational fluid dynamics is used to simulate the nozzle operating conditions. The simulation process involves solving the three-dimensional Navier-Stokes equations, calculating the flow field within the nozzle, and obtaining the nozzle exit Mach number. Ultimately, the theoretical nozzle profile is generated.

[0005] S2. Visualized nozzle processing: Based on the theoretical nozzle profile generated in S1, a flame spray system is used to perform thermoplastic processing on borosilicate glass. The nozzle motion trajectory is controlled by a CNC displacement platform, and a mapping model between flame parameters and glass deformation is established to ultimately produce a solid nozzle.

[0006] S3. PIV measurement: Uniformly spread tracer particles within the test section of the solid nozzle obtained in S2, ensuring that the particles follow the flow field. A dual-pulse laser sheet light source is used to illuminate the target area. A high-speed camera is used to synchronously capture particle images. The images are analyzed using a three-frame cross-correlation algorithm to analyze particle displacements. The 2D or 3D velocity field distribution is calculated. The 3D flow field structure is reconstructed using multi-view measurement data to extract the flow field dataset.

[0007] S4. Based on the flow field dataset from S3, compare it with the theoretical design values ​​to identify areas of flow field inhomogeneity, establish a flow field distortion index evaluation model, use a genetic algorithm to optimize the nozzle profile parameters, reprocess, and iterate the test until the flow field uniformity meets the threshold requirements.

[0008] Furthermore, in S1, the improved Sivells method is used to construct the axial Mach number distribution function by:

[0009]

[0010] Implementation, where M(x) is the axial Mach number distribution function, M0 is the initial Mach number, ΔM is the Mach number change amplitude, k is the distribution steepness parameter, x0 is the characteristic position parameter, x is the axial position coordinate;

[0011] Combined with transonic theory, the relationship between nozzle length and profile curvature is established through:

[0012]

[0013] Implementation, where R(x) is the nozzle wall curvature function, A(s) is the nozzle cross-sectional area, is the critical cross-sectional area, M is the abbreviation of axial Mach number distribution function, S is the integral path variable;

[0014] The nozzle Mach number is verified by computational fluid dynamics method.

[0015] The three-dimensional NS equations are:

[0016]

[0017] Where t is the time variable, Q is the conserved variable; F i is the inviscid flux, i=1,2,30; F vi is the viscous flux.

[0018] Furthermore, in S2, the temperature range of the flame spraying system is 1500°C-2500°C, and the jet velocity range is 50m / s-200m / s; the feed speed range of the CNC displacement platform is 0.1mm / s-5mm / s, and the spray angle range is 30°-60°.

[0019] Furthermore, in S2, the mapping model between flame parameters and glass deformation is:

[0020]

[0021] Obtained, where δ is the glass deformation, T is the flame temperature, v is the jet velocity, h is the jet height, kv is the material constant.

[0022] Furthermore, in S3, the particle displacement of the image is analyzed based on the three-frame cross-correlation algorithm:

[0023]

[0024] Implementation, where u i，j is the velocity vector component, m and n are the image pixel coordinates, u and v are the displacement vector components, I t is the image intensity at time t, and Δt is the pulse interval.

[0025] Furthermore, in S4, the flow field distortion index evaluation model is implemented by:

[0026]

[0027] Implementation, where FDI is the flow field distortion index evaluation model, is the local velocity measurement, is the average velocity of the target section, N is the number of effective velocity sampling points, and i is the index of the velocity sampling point;

[0028] Genetic algorithm is used to optimize nozzle profile parameters by:

[0029] ;

[0030]

[0031] Implementation, where θ is the design variable, C j is a constraint condition.

[0032] Beneficial effects of the present invention:

[0033] 1. Comprehensive coverage of flow field assessment blind spots: Combined with the visual nozzle design, dynamic capture of the entire flow field is achieved, reducing the spatial limitations of traditional probe measurements.

[0034] 2. Quantitatively correlate geometric errors and flow field distortion: Build a mathematical model based on PIV data to establish a direct quantitative relationship between nozzle profile errors and flow field non-uniformity, accelerating design optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a flow chart of a method for manufacturing and evaluating a high-performance wind tunnel visualization nozzle;

[0036] Figure 2 Schematic diagram of the visual nozzle installation structure. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is further described below with reference to the embodiments, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be included in the scope of protection of the present invention. The process equipment or devices not specifically noted in the following examples are all conventional equipment or devices in the art. Unless otherwise specified, the raw materials used in the examples of the present invention can be obtained commercially; unless otherwise specified, the technical means used in the examples of the present invention are all conventional means well known to those skilled in the art.

[0038] Example 1, combined Figure 1 This embodiment describes a design and evaluation method for a high-performance wind tunnel visualization nozzle, including:

[0039] S1. Nozzle Design: An improved Sivells method is used to construct an axial Mach number distribution function to calculate the wall profile. Incorporating transonic theory, a relationship between nozzle length and profile curvature is established. Computational fluid dynamics is used to simulate the nozzle operating conditions. The simulation process involves solving the three-dimensional Navier-Stokes equations, calculating the flow field within the nozzle, and obtaining the nozzle exit Mach number. Ultimately, the theoretical nozzle profile is generated.

[0040] S2. Visualized nozzle processing: Based on the theoretical nozzle profile generated in S1, a flame spray system is used to perform thermoplastic processing on borosilicate glass. The nozzle motion trajectory is controlled by a CNC displacement platform, and a mapping model between flame parameters and glass deformation is established to ultimately produce a solid nozzle.

[0041] S3. PIV measurement: Uniformly spread tracer particles within the test section of the solid nozzle obtained in S2, ensuring that the particles follow the flow field. A dual-pulse laser sheet light source is used to illuminate the target area. A high-speed camera is used to synchronously capture particle images. The images are analyzed using a three-frame cross-correlation algorithm to analyze particle displacements. The 2D or 3D velocity field distribution is calculated. The 3D flow field structure is reconstructed using multi-view measurement data to extract the flow field dataset.

[0042] S4. Based on the flow field dataset from S3, compare it with the theoretical design values ​​to identify areas of flow field inhomogeneity, establish a flow field distortion index evaluation model, use a genetic algorithm to optimize the nozzle profile parameters, reprocess, and iterate the test until the flow field uniformity meets the threshold requirements.

[0043] Specifically, Figure 2 This is a schematic diagram of the visual nozzle installation structure, which ensures the PIV laser transmittance and nozzle thermal deformation difference control requirements.

[0044] This scheme constructs the axial Mach number distribution function through the improved Sivells method and combines it with the transonic theory to derive the nozzle surface curvature, and verifies the design by solving the three-dimensional NS equations through CFD. Based on this, a flame spraying system is used to perform thermoplastic processing on borosilicate glass, and the surface error is controlled by a CNC displacement platform. In the physical nozzle test section, dual-pulse laser PIV measurement and a three-frame cross-correlation algorithm are used to reconstruct the three-dimensional velocity field. Finally, a flow field distortion index evaluation model is established, and the design parameters are optimized with a genetic algorithm and iterative processing is performed to achieve precise control of nozzle flow field unevenness.

[0045] In S1, the improved Sivells method is used to construct the axial Mach number distribution function by:

[0046]

[0047] Implementation, where M(x) is the axial Mach number distribution function, M0 is the initial Mach number, ΔM is the Mach number change amplitude, k is the distribution steepness parameter, x0 is the characteristic position parameter, x is the axial position coordinate;

[0048] Combined with transonic theory, the relationship between nozzle length and profile curvature is established through:

[0049]

[0050] Implementation, where R(x) is the nozzle wall curvature function, A(s) is the nozzle cross-sectional area, is the critical cross-sectional area, M is the abbreviation of axial Mach number distribution function, S is the integral path variable;

[0051] The nozzle Mach number is verified by computational fluid dynamics method.

[0052] The three-dimensional NS equations are:

[0053]

[0054] Where t is the time variable, Q is the conserved variable; F i is the inviscid flux, i=1,2,30; F vi is the viscous flux.

[0055] In S2, the temperature range of the flame spraying system is 1500℃-2500℃, the jet velocity range is 50m / s-200m / s; the feed speed range of the CNC displacement platform is 0.1mm / s-5mm / s, and the spray angle range is 30°-60°.

[0056] In S2, the mapping model between flame parameters and glass deformation is:

[0057]

[0058] Obtained, where δ is the glass deformation, T is the flame temperature, v is the jet velocity, h is the jet height, k v is the material constant.

[0059] Specifically, this step is based on the principle of high-temperature flame thermoplastic processing. By precisely controlling the flame temperature, jet velocity, and the spray height between the nozzle and the glass surface, a quantitative mapping model between the flame thermal parameters and the deformation of borosilicate glass is established. Combined with the material property constants, the prediction and compensation of the glass deformation are realized. Ultimately, it is ensured that the deviation between the curvature of the nozzle solid surface and the theoretical design value is reasonable, providing an optically accurate and structurally stable transparent observation window for the visualization of hypersonic flow fields.

[0060] In S3, the particle displacement of the image is analyzed based on the three-frame cross-correlation algorithm:

[0061]

[0062] Implementation, where u i，j is the velocity vector component, m and n are the image pixel coordinates, u and v are the displacement vector components, I t is the image intensity at time t, and Δt is the pulse interval.

[0063] Specifically, S3 uses a dual-pulse Nd:YAG laser (wavelength 532nm, pulse interval 5-50μs) to observe the target area; spreads TiO2 tracer particles with a particle size of 1-5μm (concentration 0.1-1g / m³); and a high-speed camera (frame rate 1000-10000fps, resolution 4096×4096) to synchronously capture images.

[0064] Furthermore, in S4, the flow field distortion index evaluation model is implemented by:

[0065]

[0066] Implementation, where FDI is the flow field distortion index evaluation model, is the local velocity measurement, is the average velocity of the target section, N is the number of effective velocity sampling points, and i is the index of the velocity sampling point;

[0067] Genetic algorithm is used to optimize nozzle profile parameters by:

[0068] ;

[0069]

[0070] Implementation, where θ is the design variable, C jis a constraint condition.

[0071] Specifically, this step constructs a quantitative model of the flow field distortion index by statistically analyzing the square of the relative deviation between the measured velocity data in the PIV measured flow field and its overall average value, accurately reflecting the global velocity uniformity; on this basis, a genetic algorithm is introduced for intelligent iteration to dynamically adjust the nozzle profile design parameters, and continuously reduce the flow field distortion index while strictly meeting the geometric manufacturing constraints and aerodynamic stability boundaries until the uniformity threshold required by the hypersonic wind tunnel is reached.

Claims

1. A design and evaluation method for a high-performance wind tunnel visualization nozzle, characterized in that: include: S1. Nozzle Design: An improved Sivells method is used to construct an axial Mach number distribution function to calculate the wall profile. Incorporating transonic theory, a relationship between nozzle length and profile curvature is established. Computational fluid dynamics is used to simulate the nozzle operating conditions. The simulation process involves solving the three-dimensional Navier-Stokes equations, calculating the flow field within the nozzle, and obtaining the nozzle exit Mach number. Ultimately, the theoretical nozzle profile is generated. S2. Visualized nozzle processing: Based on the theoretical nozzle profile generated in S1, a flame spray system is used to perform thermoplastic processing on borosilicate glass. The nozzle motion trajectory is controlled by a CNC displacement platform, and a mapping model between flame parameters and glass deformation is established to ultimately produce a solid nozzle. S3. PIV measurement: Uniformly spread tracer particles within the test section of the solid nozzle obtained in S2, ensuring that the particles follow the flow field. A dual-pulse laser sheet light source is used to illuminate the target area. A high-speed camera is used to synchronously capture particle images. The images are analyzed using a three-frame cross-correlation algorithm to analyze particle displacements. The 2D or 3D velocity field distribution is calculated. The 3D flow field structure is reconstructed using multi-view measurement data to extract the flow field dataset. S4. Based on the flow field dataset from S3, compare it with the theoretical design values ​​to identify areas of flow field inhomogeneity, establish a flow field distortion index evaluation model, use a genetic algorithm to optimize the nozzle profile parameters, reprocess, and iterate the test until the flow field uniformity meets the threshold requirements.

2. The design and evaluation method of a high-performance wind tunnel visualization nozzle according to claim 1, characterized in that: In S1, the improved Sivells method is used to construct the axial Mach number distribution function by: accomplish, Where M(x) is the axial Mach number distribution function, M0 is the initial Mach number, ΔM is the Mach number variation amplitude, k is the distribution steepness parameter, x0 is the characteristic position parameter, x is the axial position coordinate; Combined with transonic theory, the relationship between nozzle length and profile curvature is established through: accomplish, Where R(x) is the curvature function of the nozzle wall, A(s) is the cross-sectional area of ​​the nozzle, is the critical cross-sectional area, M is the abbreviation of axial Mach number distribution function, S is the integral path variable; The nozzle Mach number is verified by computational fluid dynamics method. The three-dimensional NS equations are: ； Where t is the time variable, Q is the conserved variable; F i is the inviscid flux, i=1,2,30; F vi is the viscous flux.

3. The design and evaluation method of a high-performance wind tunnel visualization nozzle according to claim 1, characterized in that: In S2, the temperature range of the flame spraying system is 1500℃-2500℃, the jet velocity range is 50m / s-200m / s; the feed speed range of the CNC displacement platform is 0.1mm / s-5mm / s, and the spray angle range is 30°-60°.

4. The method for designing and evaluating a high-performance wind tunnel visualization nozzle according to claim 1, wherein: In S2, the mapping model between flame parameters and glass deformation is: get, Where δ is the glass deformation, T is the flame temperature, v is the jet velocity, h is the jet height, k v is the material constant.

5. The method for designing and evaluating a high-performance wind tunnel visualization nozzle according to claim 1, wherein: In S3, the particle displacement of the image is analyzed based on the three-frame cross-correlation algorithm: accomplish, Among them, u i，j is the velocity vector component, m and n are the image pixel coordinates, u and v are the displacement vector components, I t is the image intensity at time t, and Δt is the pulse interval.

6. The method for designing and evaluating a high-performance wind tunnel visualization nozzle according to claim 1, wherein: In S4, the flow field distortion index evaluation model is: accomplish, Among them, FDI is the flow field distortion index evaluation model, is the local velocity measurement, is the average velocity of the target section, N is the number of effective velocity sampling points, and i is the index of the velocity sampling point; Genetic algorithm is used to optimize nozzle profile parameters by: accomplish, Among them, θ is the design variable, C j is a constraint condition.

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