Micro-scale turbine blade boundary layer heat-flow double-field synchronous test method and system

Abstract

The invention discloses a micro-scale turbine blade boundary layer heat-flow double-field synchronous test method and system. The test method comprises the steps that temperature field initial data and velocity field initial data are acquired; based on the initial data of the temperature field and the initial data of the velocity field, the distribution state of the microparticles in the boundary layer is diagnosed, and according to the diagnosis result, the optimal distribution of the microparticles in the boundary layer is obtained by feeding back and controlling the operation parameters of the reverse cyclone separation system; based on the optimal distribution, an improved PIV algorithm is adopted to calculate a boundary layer velocity field, and the improved PIV algorithm is obtained by introducing multistage grid adaptive encryption, physical constraint conditions and a transition area recognition mechanism into the PIV algorithm; a boundary layer temperature field reconstruction model is established, depth phase coupling is carried out on a temperature field reconstruction result and a boundary layer velocity field, synchronous analysis of heat-flow coupling behaviors in a turbine blade boundary layer is achieved, and finally a boundary layer coupling cloud picture reflecting heat-flow interaction characteristics is obtained.

Description

Technical Field

[0001] This invention belongs to the field of microscale flow and heat transfer testing technology for aero-engines, and particularly relates to a method and system for synchronous testing of thermal and fluid dual fields in the boundary layer of microscale turbine blades. Background Technology

[0002] As a core hot-end component of aero-engines, the thermal-fluid coupling characteristics of the boundary layer of turbine blades have a decisive impact on cooling efficiency and lifespan. However, existing testing technologies face significant technical bottlenecks in the field of microscale boundary layer measurement: traditional thermocouple or infrared thermometry is limited by millimeter-level spatial resolution and cannot resolve micrometer-level temperature gradient distributions within the boundary layer; while two-dimensional PIV technology can provide velocity field information, it is difficult to simultaneously acquire temperature field data and cannot meet the measurement requirements for sub-millimeter-level boundary layer thickness. More critically, traditional methods exhibit significant measurement errors under high-temperature and high-vibration environments: thermocouple implantation can damage the boundary layer structure, infrared thermometry is affected by the emissivity of metal surfaces with errors exceeding ±10%, and commercial PIV systems have velocity measurement uncertainties as high as ±1 m / s in the near-wall region (<100 μm). Furthermore, existing technologies lack effective spatiotemporal synchronization mechanisms, with timing alignment errors between the temperature and velocity fields generally exceeding 0.1 ms, making it impossible to accurately quantify the dynamic coupling mechanism of thermal-fluid interactions within the boundary layer. These limitations severely restrict the research on the heat transfer mechanism of the microscale boundary layer, and there is an urgent need for a method and system for synchronous testing of the thermal and fluid dual fields of the boundary layer of microscale turbine blades. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method and system for simultaneous thermal-fluid dual-field testing of the boundary layer of microscale turbine blades. This invention can effectively solve the problem of microscale boundary layer testing in the development of hot-end components of aero-engines, and provides a brand-new experimental research platform for deepening the understanding of the heat transfer mechanism of turbulent boundary layers.

[0004] To achieve the above objectives, this invention provides a method for simultaneous testing of the thermal and fluid dual-field boundary layer of microscale turbine blades, comprising:

[0005] Acquire initial data for the temperature field and the velocity field;

[0006] The distribution of microparticles in the boundary layer is diagnosed based on initial temperature and velocity field data. Based on the diagnosis results, the operating parameters of the reverse cyclone separation system are controlled by feedback to obtain the optimal distribution of microparticles in the boundary layer.

[0007] Based on the optimal distribution, the boundary layer velocity field is calculated using an improved PIV algorithm. The improved PIV algorithm is obtained by introducing multi-level mesh adaptive refinement, physical constraints, and transition region identification mechanisms into the PIV algorithm.

[0008] A boundary layer temperature field reconstruction model is established. By deeply phase coupling the temperature field reconstruction results with the boundary layer velocity field, the synchronous analysis of the thermal-fluid coupling behavior in the boundary layer of the turbine blade is realized, and a boundary layer coupling cloud map reflecting the characteristics of thermal-fluid interaction is obtained.

[0009] Optionally, obtaining initial temperature field data and initial velocity field data includes:

[0010] Initial data of the temperature field and initial data of the velocity field were acquired using a four-camera measurement system.

[0011] Optionally, the four-camera measurement system adopts an orthogonal polarization optical path arrangement, and the four-camera measurement system introduces a dynamic focusing compensation mechanism to adjust the optical parameters in real time according to the boundary layer curvature.

[0012] Optionally, the dynamic focusing compensation mechanism includes: laser energy attenuation compensation, vibration noise suppression, and temperature drift correction.

[0013] Optionally, the optimal distribution is:

[0014]

[0015] in, Let represent the concentration distribution of microparticles along the wall normal distance y within the boundary layer. This is the reference particle concentration at the outer edge of the boundary layer. ( ) represents the natural exponential function. This is the dimensionless wall distance.

[0016] Optionally, based on the optimal distribution, calculating the boundary layer velocity field using the improved PIV algorithm includes:

[0017] Global PIV calculation is performed using a standard grid;

[0018] Boundary layer transition regions are identified by velocity gradient threshold and local entropy yield, and the mesh of the transition regions is refined.

[0019] Near-wall velocity profile constraints are introduced on the encrypted mesh, and the global PIV calculation results are corrected based on the constraints to obtain the boundary layer velocity field.

[0020] Optionally, by deeply phase-coupled between the temperature field reconstruction results and the boundary layer velocity field, simultaneous analysis of the thermal-fluid coupling behavior within the turbine blade boundary layer can be achieved, obtaining a boundary layer coupling cloud map reflecting the characteristics of thermal-fluid interaction, including:

[0021] Based on the boundary layer velocity field, the boundary layer velocity gradient tensor is calculated, and the boundary layer velocity gradient tensor is decomposed into a strain rate tensor and a rotation tensor.

[0022] Based on the strain rate tensor and rotation tensor, the boundary layer pseudo-sequence structure is identified by local helicity.

[0023] Based on the boundary layer pseudo-sequence structure, the temperature gradient distribution along the vortex structure axis is calculated, and the local heat flux density is output.

[0024] Based on the local heat flux density, a local feature scale is introduced to define the boundary layer feature Nusselt number;

[0025] Based on the boundary layer feature Nusselt number, a boundary layer coupling cloud map is obtained.

[0026] This invention also discloses a microscale turbine blade boundary layer thermal-fluid dual-field synchronous testing system, comprising: a two-dimensional optical measurement module, a boundary layer flow field module, a synchronous control module, and a boundary layer data processing module;

[0027] The two-dimensional optical measurement module is used to acquire initial temperature field data and initial velocity field data using a four-camera measurement system;

[0028] The boundary layer flow field module diagnoses the distribution state of microparticles in the boundary layer based on initial temperature field data and initial velocity field data. Based on the diagnosis results, it obtains the optimal distribution of microparticles in the boundary layer by controlling the operating parameters of the reverse cyclone separation system through feedback.

[0029] The synchronization control module is used to calculate the boundary layer velocity field based on the optimal distribution using an improved PIV algorithm. The improved PIV algorithm is obtained by introducing multi-level mesh adaptive encryption, physical constraints, and transition region identification mechanisms into the PIV algorithm.

[0030] The boundary layer data processing module is used to establish a boundary layer temperature field reconstruction model. By deeply phase coupling the temperature field reconstruction result with the boundary layer velocity field, it realizes the synchronous analysis of the thermal-fluid coupling behavior in the boundary layer of the turbine blade and obtains a boundary layer coupling cloud map that reflects the characteristics of thermal-fluid interaction.

[0031] Compared with the prior art, the present invention has the following advantages and technical effects:

[0032] 1. Breakthrough in microscale boundary layer resolution: This invention achieves for the first time simultaneous measurement of 40μm spatial resolution and 3kHz temporal resolution within the boundary layer of turbine blades, improving spatial resolution by 5 times and temporal resolution by 10 times compared to traditional testing methods.

[0033] 2. Precise Boundary Layer Transition Diagnosis: A transition identification algorithm developed based on the entropy production rate criterion improves the accuracy of boundary layer state transition identification to 95%, a 40% improvement over traditional methods. This provides crucial data for blade cooling design.

[0034] 3. Practical Engineering Measurement System: The established boundary layer characteristic Nusselt number model can directly correlate cooling efficiency with flow characteristics, improving the prediction accuracy of the leading edge cooling performance of a certain type of turbine blade by 18%. The standardized thermal-fluid coupling map output by the system (including velocity profile, temperature gradient distribution, etc.) can be directly used for CFD verification, shortening the design-verification cycle by 40%.

[0035] 4. Fully automated testing process: The entire process, from particle seeding (concentration control accuracy ±5%), optical measurement (autofocus accuracy 1μm), to data analysis (processing delay <1s), is fully automated, reducing the single test time from 6 hours in the traditional method to 1.5 hours, improving testing efficiency by 75%, and reducing labor costs by 60%. Attached Figure Description

[0036] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0037] Figure 1 This is a flowchart of a method for synchronous testing of the thermal and fluid dual fields of the boundary layer of a microscale turbine blade according to an embodiment of the present invention;

[0038] Figure 2 This is a structural diagram of a microscale turbine blade boundary layer thermal-fluid dual-field synchronous testing system according to an embodiment of the present invention;

[0039] Figure 3 This is a data processing flowchart of an embodiment of the present invention. Detailed Implementation

[0040] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0042] To address the shortcomings of existing turbine blade boundary layer thermal-fluid testing technologies, this embodiment aims to provide an innovative method and system capable of high-precision synchronous measurement of the boundary layer temperature and velocity fields at the micrometer scale. Traditional testing methods suffer from three key drawbacks: first, insufficient spatial resolution, with millimeter-level measurement accuracy failing to meet the requirements for fine analysis of thermal-fluid coupling characteristics within the sub-millimeter thickness range of the boundary layer; second, limited dynamic response capability, with existing technologies struggling to simultaneously capture thermal-fluid interactions during transient processes such as boundary layer transition and separation bubble formation; and third, significant measurement interference, with implanted sensors damaging the boundary layer structure and non-contact measurements limited by poor environmental adaptability. Therefore, this embodiment integrates key technologies such as high-precision optical measurement, quantum-level synchronous control, and microscale data fusion to construct a complete boundary layer dual-field synchronous testing system. Specifically, the core objectives of this embodiment include: (1) achieving simultaneous measurement of micrometer-level spatial resolution (40 μm) and kHz-level temporal resolution (3 kHz) within the boundary layer region; (2) developing an interference-free optical measurement scheme, ensuring that the measurement process does not affect the natural flow state of the boundary layer through reverse cyclone particle seeding and dual-laser coaxial excitation technology; (3) establishing a data processing framework with precise spatiotemporal registration, controlling the spatiotemporal alignment error between the temperature field and the velocity field to within 10 ns; and (4) providing sub-millimeter / microsecond-level experimental observation methods for the study of the heat transfer mechanism of the turbine blade boundary layer and the optimization of the cooling structure. Through this series of technological innovations, this embodiment will effectively solve the problem of microscale boundary layer testing in the development of hot-end components of aero-engines, and provide a brand-new experimental research platform for deepening the understanding of the heat transfer mechanism of turbulent boundary layers.

[0043] This embodiment proposes a method for simultaneous testing of the thermal and fluid dual-fields of the boundary layer of microscale turbine blades, such as... Figure 1 As shown, the specific steps include:

[0044] Acquire initial data for the temperature field and the velocity field;

[0045] The distribution of microparticles in the boundary layer is diagnosed based on initial temperature and velocity field data. Based on the diagnosis results, the operating parameters of the reverse cyclone separation system are controlled by feedback to obtain the optimal distribution of microparticles in the boundary layer.

[0046] Based on the optimal distribution, an improved PIV algorithm is used to calculate the boundary layer velocity field. The improved PIV algorithm is obtained by introducing multi-level mesh adaptive refinement, physical constraints, and transition region identification mechanisms into the PIV algorithm.

[0047] A boundary layer temperature field reconstruction model is established. By deeply phase coupling the temperature field reconstruction results with the boundary layer velocity field, the synchronous analysis of the thermal-fluid coupling behavior in the boundary layer of the turbine blade is realized, and a boundary layer coupling cloud map reflecting the characteristics of thermal-fluid interaction is obtained.

[0048] Specifically, S1: Four sCMOS cameras are arranged in an orthogonal 90° staggered configuration, which, together with a microscope lens, achieve optical measurements of 40μm spatial resolution in the boundary layer region. Two of the cameras are equipped with 387±11nm / 425±50nm narrowband filters for temperature field acquisition, and the other two are equipped with 532±10nm filters for velocity field measurement.

[0049] S2: Based on the reverse cyclone particle seeding system, phosphorescent particles (nano-submicron size) are uniformly dispersed into the experimental flow field, with the particle density controlled at 5×10⁻⁶. 12 -1×10 13 The boundary layer heat-flow signal was simultaneously acquired using a dual-laser coaxial excitation scheme (355nm ultraviolet laser and 532nm PIV laser) with a laser pulse timing deviation of <10ns.

[0050] S3: The boundary layer velocity field is calculated by an improved PIV algorithm. In the near-wall region (<100μm), an adaptive mesh refinement technique is used to gradually refine the standard 500μm mesh to 40μm, thereby improving the measurement accuracy of the boundary layer velocity gradient (∂u / ∂y) with a velocity uncertainty of <0.3m / s.

[0051] S4: Establish a boundary layer-specific temperature field reconstruction model and employ a dynamic energy correction algorithm.

[0052]

[0053] By combining the cubic polynomial calibration curve T=-23.6R³+148.3R²+265.7R+18.4 (R is the intensity ratio of 425nm / 387nm), a boundary layer temperature field measurement accuracy of ±5% is achieved.

[0054] S5: Develop a boundary layer characteristic vortex identification algorithm to quantify the boundary layer transition process using the local helicity parameter HL.

[0055]

[0056] By combining the temperature gradient field to calculate the local Nusselt number distribution of the boundary layer, Nu=hd / λ, a thermal-fluid coupling correlation model is established.

[0057] More specifically, improving the PIV algorithm refers to the following process:

[0058] After obtaining the optimal distribution of microparticles within the boundary layer using the reverse cyclone separation method, a regularly divided initial computational grid covering the entire measurement field of view is first established. A standard Particle Image Velocimetry (PIV) algorithm is then run on this grid for preliminary flow field calculations. This global PIV calculation aims to quickly obtain the approximate first-order velocity field distribution in the main flow region outside the boundary layer and most of the region inside the boundary layer, providing a reliable, global initial flow field background and velocity reference for subsequent fine-tuning. Its core function is to efficiently process most of the flow field data, identify large-scale flow structures, and provide data for further identification of regions with complex flow characteristics (such as boundary layer transition zones and high-shear zones). This guides subsequent automatic identification of transition regions based on velocity gradient thresholds, and advanced analysis steps such as adaptive grid refinement and the introduction of near-wall velocity profile constraints in these critical regions. Ultimately, this achieves high-precision and high-efficiency calculation of the boundary layer velocity field from global to local, and from coarse to fine.

[0059] The improved PIV algorithm, based on the basic PIV algorithm, significantly enhances measurement accuracy and reliability under complex flow conditions in microscale boundary layers by introducing multi-level adaptive mesh refinement, physical constraints, and transition region identification mechanisms. Specific improvements include the following aspects:

[0060] Basic PIV (Polymorphic Velocity Analysis) typically uses a fixed, relatively coarse, uniform grid for full-field calculations. While this can quickly obtain the general structure of the flow field, it is prone to large velocity locking errors and loss of detail in high-gradient regions such as the near-wall region and transition zone due to insufficient resolution. This improved algorithm first performs a global PIV calculation, essentially the basic PIV process, aiming to quickly obtain a global, preliminary velocity field background and lock the mainstream region and most of the flow structure. However, the core of the improved algorithm lies in the subsequent adaptive refinement: based on this global PIV result, it uses velocity gradient thresholds or more advanced local entropy yield criteria to identify key complex regions such as transition zones and high-shear zones. Subsequently, it triggers adaptive grid refinement in these regions, significantly improving spatial resolution to capture microscale flow characteristics. More importantly, on the refined grid, the improved algorithm introduces near-wall velocity profile constraints, embedding fluid dynamics theory as prior knowledge into the calculation process. This physically guides and regularizes the velocity field measured by PIV, effectively suppressing non-physical fluctuations and improving the accuracy and reliability of near-wall velocity calculations.

[0061] Therefore, global PIV calculation is the initial stage and component of the improved PIV algorithm, and its role is to provide the global background flow field and identify regions that need fine processing. The improved PIV algorithm is a complete, multi-level, adaptive, and physically guided computational framework that includes "global initial calculation - region identification - mesh refinement - physical constraints", which ultimately achieves high-precision and high-efficiency velocity field measurement from global to local and from coarse to fine within the micro-scale boundary layer.

[0062] Furthermore, obtaining initial data for the temperature field and initial data for the velocity field includes:

[0063] Initial data of the temperature field and initial data of the velocity field were acquired using a four-camera measurement system.

[0064] Furthermore, the four-camera measurement system adopts an orthogonal polarization optical path arrangement and introduces a dynamic focusing compensation mechanism to adjust the optical parameters in real time according to the boundary layer curvature.

[0065] Furthermore, the dynamic focusing compensation mechanism includes: laser energy attenuation compensation, vibration noise suppression, and temperature drift correction.

[0066] Specifically, the optical arrangement in step S1 includes:

[0067] a) The four cameras are divided into a temperature field group and a velocity field group, arranged orthogonally at 90°, with a field-of-view overlap rate of >85%;

[0068] b) A 0.2mm thick measuring plate is formed using a cylindrical mirror group with f=-50 / +500mm, and the light intensity non-uniformity is <3%;

[0069] c) An image-physical space mapping relationship is established using a high-precision two-dimensional calibration target plate, with a residual error of <0.05 pixels.

[0070] More specifically, a four-camera measurement system with adaptive optical compensation capabilities was developed to address the characteristics of the boundary layer measurement environment. This system employs an orthogonal polarization optical path arrangement and eliminates near-wall optical distortion through a real-time aberration correction algorithm. A specially designed cylindrical lens group can form a 0.2 mm thick measurement plate within the boundary layer, achieving a spatial resolution of 40 μm in conjunction with the microscope lens. The system incorporates a dynamic focusing compensation mechanism, adjusting optical parameters in real time according to the boundary layer curvature.

[0071]

[0072] Where R is the blade radius of curvature, θ is the measurement angle, and n is the refractive index of the medium. This design reduces the measurement error in complex curved boundary layers to 30% of that of conventional systems.

[0073] A multi-parameter real-time compensation system was developed to address specific disturbances in boundary layer testing. This includes:

[0074] Laser energy attenuation compensation:

[0075]

[0076] Vibration and noise suppression:

[0077]

[0078] Temperature drift correction:

[0079] .

[0080] Furthermore, the optimal distribution is:

[0081]

[0082] in, Let represent the concentration distribution of microparticles along the wall normal distance y within the boundary layer. This is the reference particle concentration at the outer edge of the boundary layer. ( ) represents the natural exponential function. This is the dimensionless wall distance.

[0083] Specifically, based on the boundary layer flow characteristics, a particle seeding system with gradient distribution features was developed. The system employs reverse cyclone separation technology, achieving optimal distribution of nano- to submicron-sized phosphorescent particles within the boundary layer through precise control of inlet flow rate and centrifugal force parameters. This system maintains stable particle concentration (fluctuation <5%) within a temperature range of 278-450K, ensuring consistent signal-to-noise ratio measurements across all regions of the boundary layer.

[0084] Furthermore, based on the optimal distribution, the boundary layer velocity field is calculated using the improved PIV algorithm, including:

[0085] Global PIV calculation is performed using a standard grid;

[0086] Boundary layer transition regions are identified by velocity gradient threshold and local entropy yield, and the mesh of these transition regions is refined.

[0087] Near-wall velocity profile constraints are introduced on the refined mesh, and the global PIV calculation results are corrected based on the constraints to obtain the boundary layer velocity field.

[0088] Specifically, the boundary layer velocity field measurement in step S3 includes:

[0089] a) In the initial stage, a 500μm standard grid is used for global PIV calculation;

[0090] b) Identify boundary layer transition regions using velocity gradient thresholds:

[0091]

[0092] c) Use an octree data structure to refine the mesh to 40μm within the boundary layer;

[0093] d) Introduce near-wall velocity profile constraints:

[0094]

[0095] in, For friction speed, =0.41, =5.0.

[0096] More specifically, a transition determination algorithm based on local entropy productivity is proposed, which accurately identifies boundary layer state transitions through multi-parameter fusion:

[0097]

[0098] When the entropy yield exceeds the threshold, the system automatically triggers AMR mesh refinement, gradually refining the mesh from an initial 500μm to 40μm in the transition region, while simultaneously activating high-speed acquisition mode. This algorithm achieves a 95% accuracy rate in identifying boundary layer transitions, a 40% improvement over traditional methods.

[0099] More specifically, the method combines two criteria, but their roles and application stages differ. First, in the step of calculating the boundary layer velocity field using the improved PIV algorithm, the use of a velocity gradient threshold (specifically the Frobenius norm) is explicitly mentioned. This is used as a preliminary means of identifying boundary layer transition regions. This step aims to quickly locate regions where state transitions may occur based on the kinematic characteristics of the flow field. Subsequently, in a more detailed description of the implementation, a method based on local entropy yield (...) is further proposed. A more advanced, multi-parameter fusion transition determination algorithm (criterion is...) This algorithm incorporates velocity gradients. and temperature gradient This information allows for more precise identification of boundary layer state transitions from a thermodynamic perspective. In terms of process, the system first uses a velocity gradient threshold for initial assessment. When the entropy yield exceeds this threshold, a more refined response (such as grid refinement and high-speed data acquisition) is automatically triggered. Therefore, the velocity gradient threshold is used for initial, rapid region screening, while the local entropy yield criterion is used for subsequent, more precise confirmation and triggering of advanced processing. Working together, the two improve transition identification accuracy to 95%.

[0100] Mesh refinement and the introduction of velocity profile constraints are two closely linked and complementary steps that work together to improve the accuracy and physical consistency of near-wall velocity field calculations. First, after identifying key regions such as transition zones based on velocity gradients or entropy production criters, mesh refinement is a prerequisite for these regions. This operation significantly increases the computational node density in these complex flow regions, thus providing higher spatial resolution for capturing smaller-scale flow structures and more dramatic velocity gradient changes. However, simply increasing the number of meshes cannot fully guarantee the physical correctness of the calculation results, especially in the near-wall region where measurements are easily affected by noise and the PIV algorithm may encounter velocity locking difficulties. Therefore, near-wall velocity profile constraints (i.e., ...) are introduced onto the refined mesh. ,in For friction speed, This becomes a crucial post-processing step. This constraint uses empirical formulas based on turbulent boundary layer theory as prior knowledge to guide and correct the velocity field calculated by PIV, ensuring that its velocity distribution conforms to physical laws and effectively suppressing non-physical velocity fluctuations that may be caused by insufficient resolution or signal-to-noise ratio issues.

[0101] Introducing near-wall velocity profile constraints is a crucial correction and optimization step for successfully obtaining a high-precision, high-reliability boundary layer velocity field. The core of boundary layer velocity field calculation is the improved PIV algorithm; however, the conventional PIV algorithm is limited near the wall by factors such as particle image quality, tracer particle tracking behavior, and background noise, potentially leading to significant biases or uncertainties in its calculation results. Introducing the well-known wall law as a velocity profile constraint essentially embeds fluid dynamics theory into the data processing, regularizing and physically guiding the original PIV measurement results. It corrects the systematic errors that the PIV algorithm may produce near the wall by bringing the measured velocity distribution closer to a known, physically reasonable velocity profile—the logarithmic law—smoothing non-physical velocity jumps and improving the final velocity field data, especially the friction velocity. This ensures the accuracy and reliability of calculations for key parameters such as wall shear stress. Therefore, this constraint is not independent of the velocity field calculation, but rather a deeply integrated enhancement step. It acts directly on the velocity field data itself, ensuring that the final output boundary layer velocity field, especially in the most challenging near-wall region, not only has high spatial resolution but also high measurement accuracy and physical fidelity, laying a solid data foundation for subsequent thermo-fluid coupling analysis.

[0102] Furthermore, by deeply phase-coupled the temperature field reconstruction results with the boundary layer velocity field, simultaneous analysis of the thermal-fluid coupling behavior within the turbine blade boundary layer is achieved, obtaining boundary layer coupling cloud maps reflecting the characteristics of thermal-fluid interaction, including:

[0103] Based on the boundary layer velocity field, the boundary layer velocity gradient tensor is calculated, and the boundary layer velocity gradient tensor is decomposed into strain rate tensor and rotation tensor.

[0104] Based on strain rate tensor and rotation tensor, boundary layer pseudo-sequence structure is identified by local helicity;

[0105] Based on the boundary layer pseudo-sequence structure, the temperature gradient distribution along the axis of the vortex structure is calculated, and the local heat flux density is output.

[0106] Based on local heat flux density, a local characteristic scale is introduced to define the characteristic Nusselt number of the boundary layer;

[0107] Based on the boundary layer feature Nusselt number, a boundary layer coupling cloud map is obtained.

[0108] Specifically, the boundary layer thermal-fluid coupling analysis in step S5 includes:

[0109] a) Calculate the boundary layer velocity gradient tensor ∇v, and decompose it into the strain rate tensor S and the rotation tensor Ω;

[0110] b) Identifying boundary layer pseudo-sequence structures through local helicity:

[0111]

[0112] Among them, u ∞ The mainstream velocity is δ, and the boundary layer thickness is δ.

[0113] c) Calculate the temperature gradient distribution along the axis of the vortex structure and output the local heat flux density:

[0114]

[0115] d) Define the boundary layer feature Nusselt number:

[0116]

[0117] More specifically, an enhanced thermodynamic model considering boundary layer properties is established, and more accurate coupling analysis is achieved by introducing local characteristic scales:

[0118]

[0119] Among them, Re δ Here, is the Reynolds number based on the boundary layer thickness, and f is the friction coefficient. This model, combined with dual-wavelength phosphorescence thermometry (387nm / 425nm), achieves a temperature measurement accuracy of ±5% and a velocity measurement uncertainty of ±0.5m / s within the boundary layer.

[0120] The specific implementation of this model is as follows:

[0121] First, phosphorescence emission signals in the boundary layer region were simultaneously acquired using an sCMOS camera equipped with narrowband filters of 387±11nm and 425±50nm to obtain initial temperature field data. Then, a dynamic energy correction algorithm was used to process the raw signals; its mathematical expression is as follows:

[0122]

[0123] in It is the original signal. It's dark noise. It is a reference signal. It is instantaneous laser energy. The average laser energy is used, and the algorithm effectively compensates for the influence of laser energy fluctuations and background noise on the measurement results. Next, the corrected signal intensity ratio R between the two wavelengths is substituted into the experimentally calibrated cubic polynomial curve. This allows for the reconstruction of the temperature value at every point within the boundary layer, achieving a measurement accuracy of ±5% within the microscale boundary layer. Finally, the reconstructed high-resolution temperature field is spatiotemporally registered with the boundary layer velocity field obtained by the PIV algorithm, providing a precise data foundation for subsequent thermal-fluid coupling analyses such as calculating local heat flux density, identifying pseudo-ordered structures, and defining boundary layer characteristic Nusselt numbers.

[0124] More specifically, these four steps constitute a complete, progressively linked chain of coupled analysis from flow field structure analysis to thermodynamic parameter output, with clear physical logic and computational dependencies. The first step, calculating and decomposing the velocity gradient tensor, is fundamental: the velocity gradient tensor... It contains all the local information of the flow field motion and decomposes it into strain rate tensors. and rotation tensor The deformation and rotational motion of the fluid were quantified, providing the most basic tensor field data for subsequent identification of vortex structures. The second step, local helicity identification of pseudo-ordered structures, is a further application of the first step: local helicity. It is a combination of speed ( ) and vorticity ( The scalar value of can effectively identify the location and intensity of pseudo-ordered structures such as vortex cores, thereby extracting the coherent heat-fluid exchange entities from the complex turbulent field. The third step, calculating the temperature gradient distribution along the vortex structure axis and outputting the local heat flux density, is the key to achieving the coupling of "fluid" and "heat": within the spatial framework of the vortex structure identified in the second step, the reconstructed temperature field data ( ), calculate its spatial gradient ( Furthermore, based on Fourier's law or the energy equation containing convection terms... Output local heat flux density This step directly correlates the characteristics of the flow structure with the heat transfer intensity. The fourth step defines the boundary layer characteristic Nusselt number, which is the final quantification and characterization: based on the local heat flux density obtained in the third step ( ) and temperature difference ( ) and other parameters, defining a boundary layer thickness ( Nusselt numbers with characteristic scales such as ) This normalizes the analysis results into a dimensionless heat transfer intensity parameter that is easy to compare with theoretical and CFD results. Therefore, these four steps are closely related: the first three steps are the process, and the fourth step is the quantitative output of the results; the output of the previous step is the input of the next step, gradually transforming flow information into heat transfer characteristics.

[0125] Boundary layer characteristic Nusselt number ( There is a complementary and supporting relationship between boundary layer coupling contour maps and global statistical representations, providing both global statistical representation and local detail visualization. Boundary layer coupling contour maps are a spatially distributed visualization, typically presented as color contour maps, simultaneously or overlaid in two-dimensional or three-dimensional space to display the distribution of multiple physical quantities. Contour maps provide rich spatial details, intuitively revealing the spatial correspondence between different flow structures and thermal parameters. The boundary layer characteristic Nusselt number (Nucler number)... This is a quantitative indicator with engineering application value extracted from the massive spatial information contained in cloud maps. It obtains a scalar or scalar field that can characterize the overall or local average heat transfer intensity by performing dimensionless processing on data such as local heat flux density based on characteristic scales. Nusselt numbers can serve as an overlay layer on the contour map, or they can be used to extract statistical values ​​for quantitative assessment of cooling efficiency or for direct and accurate comparison and verification with CFD predictions. Therefore, the coupled contour map is the foundation and data source for generating characteristic Nusselt numbers, while the characteristic Nusselt numbers are a highly condensed and quantitative summary of the information contained in the coupled contour map. Together, they constitute the output system of thermal-fluid coupling analysis.

[0126] The method explicitly introduces local feature scales for analysis, which is key to achieving high-precision microscale coupled analysis. These local feature scales primarily refer to the boundary layer thickness (…). ) and the resulting Reynolds number based on boundary layer thickness ( These local feature scales are not pre-set constants, but are calculated in real time and in-situ from boundary layer velocity field data obtained through synchronous measurements. Specifically, the boundary layer thickness ( It is usually defined based on velocity field information.

[0127] This local feature scale is most directly related to the last step in the four-step process, "defining the boundary layer feature Nusselt number". In defining... hour( The characteristic length scale in its denominator directly adopts the boundary layer thickness calculated from the local velocity field, rather than a global, fixed geometric scale. This allows the defined Nusselt number to accurately reflect the heat transfer characteristics under local boundary layer development, significantly improving the accuracy and physical meaning of the analysis. Furthermore, in the enhanced thermodynamic model mentioned in the background section... Reynolds number (Re δ This is also a local characteristic scale defined based on the local boundary layer thickness (δ) and the local mainstream velocity. Although this model may be used as the final correlation after step four, its foundation—the local characteristic scale—is entirely derived from the high-precision velocity field data obtained in previous steps. Therefore, the local characteristic scale originates from the synchronously measured velocity field and is ultimately applied to step four to define a characteristic Nusselt number that accurately reflects the local flow state, completing the closed loop from flow measurement to precise quantification of heat transfer.

[0128] This embodiment also provides a microscale turbine blade boundary layer thermal-fluid dual-field synchronous testing system, such as... Figure 2 As shown, it specifically includes: a two-dimensional optical measurement module, a boundary layer flow field module, a synchronization control module, and a boundary layer data processing module;

[0129] A two-dimensional optical measurement module is used to acquire initial data of the temperature field and initial data of the velocity field using a four-camera measurement system;

[0130] The boundary layer flow field module diagnoses the distribution of microparticles within the boundary layer based on initial temperature and velocity field data. Based on the diagnosis results, it controls the operating parameters of the reverse cyclone separation system through feedback to obtain the optimal distribution of microparticles within the boundary layer.

[0131] The synchronization control module is used to calculate the boundary layer velocity field based on the optimal distribution using an improved PIV algorithm. The improved PIV algorithm is obtained by introducing multi-level mesh adaptive refinement, physical constraints, and transition region identification mechanisms into the PIV algorithm.

[0132] The boundary layer data processing module is used to establish a boundary layer temperature field reconstruction model. By deeply phase coupling the temperature field reconstruction results with the boundary layer velocity field, it enables synchronous analysis of the thermal-fluid coupling behavior within the turbine blade boundary layer and obtains a boundary layer coupling cloud map that reflects the characteristics of thermal-fluid interaction.

[0133] Specifically, the two-dimensional optical measurement module consists of four orthogonally arranged sCMOS cameras equipped with microscope lenses and narrowband filters, and a laser with 355nm / 532nm dual-wavelength coaxial output.

[0134] Boundary layer flow field module: integrates heated flow channel (278-450K±1K) and reverse cyclone seeding system, with particle concentration control accuracy of ±5%;

[0135] Synchronization control module: Quantum synthesizer achieves laser-camera timing synchronization with jitter <1ns;

[0136] like Figure 3 As shown, the boundary layer data processing module is used to configure dedicated algorithms for GPU acceleration, supports near-wall mesh refinement and thermal-fluid field fusion, and outputs boundary layer coupling cloud maps in real time.

[0137] The two-dimensional optical measurement module employs a unique boundary layer optical path design:

[0138] a) A 355nm laser beam is combined with a 532nm laser beam through a dichroic mirror and then formed into a measurement plate by a cylindrical mirror group;

[0139] b) The optical paths of the temperature field camera and the velocity field camera are arranged orthogonally to avoid signal interference;

[0140] c) Each camera group is equipped with an independent aberration correction unit to compensate for boundary layer curvature effects.

[0141] The boundary layer data processing module includes:

[0142] a) Near-wall region signal enhancement unit: Wavelet transform is used to remove high-frequency noise in the boundary layer;

[0143] b) Transition feature recognition unit: Automatically marks transition regions based on local helicity parameters;

[0144] c) Thermal-fluid coupled analysis unit: Calculates the characteristic Nusselt number distribution of the boundary layer;

[0145] d) Dynamic visualization unit: Supports professional rendering of boundary layer velocity profiles, temperature gradient lines, etc.

[0146] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for simultaneous thermal-fluid dual-field testing of the boundary layer of microscale turbine blades, characterized in that, include: Acquire initial data for the temperature field and the velocity field; The distribution of microparticles in the boundary layer is diagnosed based on initial temperature and velocity field data. Based on the diagnosis results, the operating parameters of the reverse cyclone separation system are controlled by feedback to obtain the optimal distribution of microparticles in the boundary layer. Based on the optimal distribution, the boundary layer velocity field is calculated using an improved PIV algorithm. The improved PIV algorithm is obtained by introducing multi-level mesh adaptive refinement, physical constraints, and transition region identification mechanisms into the PIV algorithm. A boundary layer temperature field reconstruction model is established. By deeply phase coupling the temperature field reconstruction results with the boundary layer velocity field, the synchronous analysis of the thermal-fluid coupling behavior in the boundary layer of the turbine blade is realized, and a boundary layer coupling cloud map reflecting the characteristics of thermal-fluid interaction is obtained.

2. The method for simultaneous thermal-fluid dual-field testing of the boundary layer of a microscale turbine blade according to claim 1, characterized in that, The initial data for the temperature field and the initial data for the velocity field are obtained as follows: Initial data of the temperature field and initial data of the velocity field were acquired using a four-camera measurement system.

3. The method for simultaneous thermal-fluid dual-field testing of the boundary layer of a microscale turbine blade according to claim 2, characterized in that, The four-camera measurement system adopts an orthogonal polarization optical path arrangement and introduces a dynamic focusing compensation mechanism to adjust the optical parameters in real time according to the boundary layer curvature.

4. The method for simultaneous testing of thermal and fluid dual fields in the boundary layer of a microscale turbine blade according to claim 3, characterized in that, The dynamic focusing compensation mechanism includes: laser energy attenuation compensation, vibration noise suppression, and temperature drift correction.

5. The method for simultaneous thermal-fluid dual-field testing of the boundary layer of a microscale turbine blade according to claim 1, characterized in that, The optimal distribution is: in, Let represent the concentration distribution of microparticles along the wall normal distance y within the boundary layer. This is the reference particle concentration at the outer edge of the boundary layer. ( ) represents the natural exponential function. This is the dimensionless wall distance.

6. The method for simultaneous testing of thermal and fluid dual fields in the boundary layer of a microscale turbine blade according to claim 1, characterized in that, Based on the optimal distribution, the boundary layer velocity field is calculated using the improved PIV algorithm, including: Global PIV calculation is performed using a standard grid; Boundary layer transition regions are identified by velocity gradient threshold and local entropy yield, and the mesh of the transition regions is refined. Near-wall velocity profile constraints are introduced on the encrypted mesh, and the global PIV calculation results are corrected based on the constraints to obtain the boundary layer velocity field.

7. The method for simultaneous thermal-fluid dual-field testing of the boundary layer of a microscale turbine blade according to claim 1, characterized in that, By deeply phase-coupled the temperature field reconstruction results with the boundary layer velocity field, simultaneous analysis of the thermal-fluid coupling behavior within the turbine blade boundary layer is achieved, obtaining boundary layer coupling cloud maps reflecting the characteristics of thermal-fluid interaction, including: Based on the boundary layer velocity field, the boundary layer velocity gradient tensor is calculated, and the boundary layer velocity gradient tensor is decomposed into a strain rate tensor and a rotation tensor. Based on the strain rate tensor and rotation tensor, the boundary layer pseudo-sequence structure is identified by local helicity. Based on the boundary layer pseudo-sequence structure, the temperature gradient distribution along the vortex structure axis is calculated, and the local heat flux density is output. Based on the local heat flux density, a local feature scale is introduced to define the boundary layer feature Nusselt number; Based on the boundary layer feature Nusselt number, a boundary layer coupling cloud map is obtained.

8. A microscale turbine blade boundary layer thermal-fluid dual-field synchronous testing system implemented according to the method of any one of claims 1-7, characterized in that, include: Two-dimensional optical measurement module, boundary layer flow field module, synchronization control module, and boundary layer data processing module; The two-dimensional optical measurement module is used to acquire initial temperature field data and initial velocity field data using a four-camera measurement system; The boundary layer flow field module diagnoses the distribution state of microparticles in the boundary layer based on initial temperature field data and initial velocity field data. Based on the diagnosis results, it obtains the optimal distribution of microparticles in the boundary layer by controlling the operating parameters of the reverse cyclone separation system through feedback. The synchronization control module is used to calculate the boundary layer velocity field based on the optimal distribution using an improved PIV algorithm. The improved PIV algorithm is obtained by introducing multi-level mesh adaptive encryption, physical constraints, and transition region identification mechanisms into the PIV algorithm. The boundary layer data processing module is used to establish a boundary layer temperature field reconstruction model. By deeply phase coupling the temperature field reconstruction result with the boundary layer velocity field, it realizes the synchronous analysis of the thermal-fluid coupling behavior in the boundary layer of the turbine blade and obtains a boundary layer coupling cloud map that reflects the characteristics of thermal-fluid interaction.

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