Two-dimensional multi-mode turbine blade heat-flow double-field synchronous test method and system

Abstract

The invention relates to the technical field of aeroengine turbine blade thermodynamic performance testing, in particular to a two-dimensional multi-mode turbine blade heat-flow double-field synchronous testing method and system.The method comprises the steps that phosphorescent particles are evenly scattered in an experimental flow field of a target turbine blade, and after the phosphorescent particles are excited through double lasers, the target turbine blade is subjected to heat-flow double-field synchronous testing; collecting a flow field image; inverting a temperature field by using a phosphorescence intensity ratio method based on the flow field image, and calculating a velocity field through an improved PIV algorithm; and after time-space registration is carried out on the temperature field data and the velocity field data, fusion is carried out to generate a composite heat-flow field, dynamic coupling characteristics are analyzed through a vortex center tracking algorithm, and the heat-flow double-field synchronous test of the target turbine blade is completed. Through ultraviolet / PIV dual-laser excitation, multispectral separation acquisition and high-precision data fusion, time-space synchronous measurement of the temperature field and the velocity field of the boundary layer of the turbine blade is realized.

Description

Technical Field

[0001] This invention relates to the field of thermodynamic performance testing technology for aero-engine turbine blades, and in particular to a two-dimensional multimodal turbine blade thermal-fluid dual-field synchronous testing method and system. Background Technology

[0002] Turbine blades face severe thermal load challenges in high-temperature combustion environments, and the performance of their cooling structures directly affects engine efficiency and lifespan. Existing testing technologies have significant limitations: traditional thermocouples or infrared thermometry can only acquire single-point temperature data, failing to capture the entire field distribution; while traditional particle image velocimetry (PIV) can obtain flow field velocity information, it is difficult to synchronously correlate with temperature field changes. Key phenomena such as boundary layer separation and hot spot formation require coupled thermal-fluid field analysis, but existing separate measurement methods suffer from timing errors and spatial registration problems. Therefore, to address the limitations of single-field measurement, insufficient spatiotemporal synchronization, weak anti-interference capabilities, and insufficient resolution and accuracy in the thermodynamic testing of aero-engine turbine blades, there is an urgent need to develop a high-precision, high-spatiotemporal-synchronous thermal-fluid field synchronous testing method and system. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for synchronous testing of the thermal and fluid dual fields of two-dimensional multimodal turbine blades. By using ultraviolet / PIV dual laser excitation, multispectral separation and acquisition, and high-precision data fusion, the spatiotemporal synchronous measurement of the temperature field and velocity field of the turbine blade boundary layer can be achieved.

[0004] To achieve the above objectives, on the one hand, the present invention provides a method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades, comprising:

[0005] Phosphorescent particles were uniformly seeded in the experimental flow field of the target turbine blade. After the phosphorescent particles were excited by dual lasers, flow field images were acquired.

[0006] Based on the flow field image, the temperature field is inverted using the phosphorescence intensity ratio method, and the velocity field is calculated using the improved PIV algorithm.

[0007] After spatiotemporal registration of temperature and velocity field data, they are fused to generate a composite thermal-fluid field. The dynamic coupling characteristics are then analyzed using a vortex core tracking algorithm to complete the synchronous test of the target turbine blade's thermal-fluid dual fields.

[0008] Optionally, seeding phosphorescent particles within the experimental flow field of the target turbine blade includes:

[0009] Phosphorescent particles with a particle size of nanometer to submicron were uniformly dispersed into the experimental flow field using a reverse cyclone seeding system, wherein the particle density was controlled between 6 × 10¹¹ and 5 × 10¹¹. 2 particles / m³.

[0010] Optionally, exciting the phosphorescent particles using dual lasers includes:

[0011] The phosphorescent particles are excited by a time-division multiplexing technique using an ultraviolet laser and an Nd:YAG laser. The ultraviolet laser is used to excite the temperature-sensitive phosphorescent signal of the phosphorescent particles, and the Nd:YAG laser is used for PIV velocimetry.

[0012] Optionally, acquiring the flow field image includes:

[0013] Four cameras were used to simultaneously acquire flow field images. Two cameras were equipped with filters of 387±11nm and 425±50nm respectively for temperature field measurement, and the other two cameras were equipped with filters of 532±10nm for velocity field measurement.

[0014] Optionally, the temperature field can be retrieved using the phosphorescence intensity ratio method, including:

[0015] After performing non-uniformity correction on the dual-channel image, the intensity ratio of phosphorescence in the dual-channel image is calculated;

[0016] Based on the intensity ratio, the temperature is retrieved using a pre-calibrated inversion model, which is:

[0017] T=-23.6R³+148.3R²+265.7R+18.4;

[0018] Where T is temperature and R is phosphorescence intensity ratio.

[0019] Optionally, calculating the velocity field using the improved PIV algorithm includes:

[0020] The first frame uses a 64×64 pixel window for initial cross-correlation calculation, and subsequent frames use a 32×32 pixel adaptive deformable mesh, combined with particle trajectory prediction technology for calculation speed.

[0021] Optionally, analyzing dynamic coupling characteristics using the vortex core tracking algorithm includes:

[0022] Based on velocity field data, the location of the vortex core is identified by the local velocity gradient tensor, and the temperature field data is correlated to calculate the local Nusselt number, thereby analyzing the transient thermal-fluid coupling characteristics of the vortex shedding process.

[0023] On the other hand, the present invention also provides a two-dimensional multimodal turbine blade thermal-fluid dual-field synchronous testing system, comprising:

[0024] Particle seeding module: used to uniformly seed phosphorescent particles in the experimental flow field of the target turbine blade;

[0025] Optical measurement module: used to acquire flow field images through multiple cameras after the phosphorescent particles are excited by dual lasers;

[0026] Synchronization control module: Used to control the timing synchronization of dual lasers and multiple cameras;

[0027] Data processing module: Based on the flow field image, it uses the phosphorescence intensity ratio method to invert the temperature field and the improved PIV algorithm to calculate the velocity field. After spatiotemporal registration of the temperature field and velocity field data, they are fused to generate a composite thermal-flow field. The dynamic coupling characteristics are analyzed by the vortex core tracking algorithm to complete the synchronous test of the thermal-flow dual field of the target turbine blade.

[0028] The beneficial effects of this invention are as follows:

[0029] This invention utilizes a dual-laser excitation architecture (UV / PIV) and a four-camera spectral separation acquisition system, combined with a heat-fluid data fusion algorithm, to achieve spatiotemporal synchronous measurement of the temperature and velocity fields of the turbine blade boundary layer. The measurement is highly accurate and can visualize the two-dimensional transient and dynamic heat-fluid coupling characteristics, providing high-precision dynamic data support for the optimization of turbine blade cooling structures. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the system configuration according to an embodiment of the present invention;

[0032] Figure 2 This is a system configuration and workflow diagram according to an embodiment of the present invention;

[0033] Figure 3 This is a flowchart of a two-dimensional multimodal turbine blade thermal-fluid dual-field synchronous testing method according to an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] This embodiment provides a method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades, such as... Figure 3 As shown, it includes:

[0037] Phosphorescent particles were uniformly seeded in the experimental flow field of the target turbine blade. After the phosphorescent particles were excited by dual lasers, flow field images were acquired.

[0038] Based on the flow field image, the temperature field is inverted using the phosphorescence intensity ratio method, and the velocity field is calculated using the improved PIV algorithm.

[0039] After spatiotemporal registration of temperature and velocity field data, they are fused to generate a composite thermal-fluid field. The dynamic coupling characteristics are then analyzed using a vortex core tracking algorithm to complete the synchronous test of the target turbine blade's thermal-fluid dual fields.

[0040] Specifically, the testing method provided in this embodiment utilizes a dual-laser excitation architecture (UV / PIV) and a four-camera spectral separation acquisition system, combined with a heat-fluid data fusion algorithm, to achieve spatiotemporal synchronous measurement of the temperature and velocity fields of the turbine blade boundary layer. This method offers high measurement accuracy and can visualize two-dimensional transient and dynamic heat-fluid coupling characteristics, providing high-precision dynamic data support for turbine blade cooling structure optimization. The specific steps include:

[0041] S1: Phosphorescent particles (nanometer to submicron size) were uniformly dispersed into the experimental flow field using a reverse cyclone seeding system, with the particle density controlled at 6×10¹¹~5×10¹¹. 12 particles / m³;

[0042] S2: A dual-laser excitation scheme is adopted. A 355nm ultraviolet laser excites the temperature-sensitive phosphorescence signal of the particles (387nm / 425nm dual-band), and a 532nm laser is used for PIV velocimetry. The two lasers are output coaxially through a dichroic mirror to form a 0.2mm thick measurement plate. The dual-laser excitation scheme adopts time-division multiplexing technology. The 355nm ultraviolet laser has a pulse width of 10ns and a repetition frequency of 3kHz, and the 532nm laser has a pulse width of 8ns and a repetition frequency of 3kHz. Time synchronization is achieved through a quantum synthesizer.

[0043] S3: Four sCMOS cameras are used to synchronously acquire signals. Two of them are equipped with narrowband filters (387±11nm and 425±50nm) for temperature field measurement, and the other two are equipped with 532±10nm filters for velocity field measurement. The timing synchronization error is <10ns.

[0044] S4: The temperature field is inverted based on the phosphorescence intensity ratio method. The velocity field is calculated by the improved PIV algorithm. The spatiotemporal registration of the dual-field data is achieved by the B-spline interpolation algorithm, with a spatial registration error of <0.2 pixels. The temperature field inversion adopts a cubic polynomial calibration curve: T = -23.6R³ + 148.3R² + 265.7R + 18.4, where R is the phosphorescence intensity ratio of the 425nm and 387nm channels.

[0045] S5: The temperature and velocity field data are fused to generate a composite thermo-fluid field containing temperature gradients and velocity vectors, and the dynamic coupling characteristics are analyzed using the vortex core tracking algorithm. The vortex core tracking algorithm identifies the vortex core position through the local velocity gradient tensor and correlates it with the temperature field data to analyze the transient thermo-fluid coupling characteristics of the vortex shedding process.

[0046] In step S1, phosphorescent particles with a particle size of nanometer to submicron are used as the dual-field measurement medium. The phosphorescence intensity ratio of the 387nm / 425nm dual-band is strictly monotonic with temperature (278~450K). Uniform particle dispersion is achieved through a reverse cyclone seeding system: tangential air intake (flow rate >100L / min) forms a stable vortex in the centrifugal separation chamber, and the particle density is precisely controlled between 6×10¹¹ and 5×10¹¹ using a particle concentration monitor. 12 Particles / m³, coefficient of variation <5%. The dual-channel independent heating system can simulate the temperature gradient of the actual working conditions of the blade. The main channel and secondary channel adopt armored heaters and are equipped with PID temperature control arrays, with a temperature control accuracy of ±5K.

[0047] In step S2, a time-division multiplexed dual-laser excitation architecture is adopted: a 355nm ultraviolet laser excites temperature-sensitive phosphorescence, and a 532nm Nd:YAG laser is used for PIV velocimetry. The two laser beams are coaxially output through a dichroic mirror and formed into a 0.2mm thick measuring plate by a cylindrical mirror group with f=-50 / +500mm, with a light intensity non-uniformity of <3%.

[0048] In step S3, four sCMOS cameras are used: the temperature field channel employs a combination of 387±11nm and 425±50nm narrowband filters, combined with a Tamron 60mm f / 2.0 microscope lens to achieve a resolution of 40μm; the velocity field channel is equipped with a 532±10nm filter, covering a field of view of 50×50mm². A quantum synthesizer controls timing synchronization, verified by a high-speed photodiode, with a timing error of <5ns.

[0049] In step S4, an adaptive noise suppression technique, namely an improved wavelet-frequency domain composite denoising algorithm, is used for preprocessing: first, a particle-free background image is acquired, then decomposed into 5 layers using the Db4 wavelet, followed by hard thresholding and Fourier domain Wiener filtering, improving the signal-to-noise ratio by 8dB. In temperature field reconstruction, after non-uniformity correction of the dual-channel image, the intensity ratio R=I for each pixel is calculated. 425 / I 387 The calibration curve, obtained by pre-calibrating the cubic polynomial T = -23.6R³ + 148.3R² + 265.7R + 18.4 for inversion temperature, was verified in a blackbody furnace within the range of 293-488K, with a nonlinearity error of <0.3%. The velocity field calculation employed an improved PIV algorithm: the first frame used a 64×64 pixel window for initial cross-correlation calculation, and subsequent frames used a 32×32 pixel adaptive deformable mesh. Combined with particle trajectory prediction technology, the measurement uncertainty in a 15 m / s high-speed flow field was <0.3 m / s. Spatiotemporal registration was achieved by unifying the coordinate system of multiple cameras using a high-precision calibration target. B-spline interpolation was used to compensate for the 0.1 ms sampling time difference, resulting in a final spatial registration error of <0.12 pixels.

[0050] In step S4, an adaptive noise suppression technique is employed. Its core lies in combining multi-scale signal processing with frequency domain filtering to effectively suppress noise in the image while preserving as much structural information of the useful signal as possible. This algorithm exhibits clear adaptive characteristics, manifested in its ability to dynamically adjust the denoising strategy according to the local noise characteristics of the image, demonstrating strong robustness, especially when processing phosphorescent particle images with non-stationary noise characteristics.

[0051] Specifically, the algorithm first acquires a particle-free background image to obtain the inherent noise structure and intensity distribution of the system, establishing a noise baseline. Then, it uses the Db4 wavelet basis function to perform a five-level decomposition of the image, leveraging the multi-resolution analysis capabilities of wavelet transform to divide the image into different frequency sub-bands. Within each level, high-frequency coefficients are filtered using hard thresholding, eliminating noise components below the threshold and retaining significant signal features. Subsequently, Wiener filtering is applied in the Fourier domain, utilizing statistical properties in the frequency domain to further suppress residual noise, achieving a systematic improvement in the image's signal-to-noise ratio.

[0052] The improved PIV algorithm in step S4 is an important optimization of the traditional particle image velocimetry method. Its core improvements are reflected in the combination of window adaptive adjustment mechanism, deformable mesh technology and particle trajectory prediction, which aims to improve the spatial resolution, accuracy and computational efficiency of velocity field measurement under high-speed flow field conditions.

[0053] Specifically, the algorithm uses a large 64×64 pixel window for initial cross-correlation calculations in the first frame. This helps obtain a more stable velocity estimate when the overall flow field structure is not yet clear, avoiding mismatches or noise sensitivity caused by excessively small windows. In subsequent frames, it switches to an adaptive deformable 32×32 pixel grid. This grid can dynamically adjust its shape and size according to the local velocity gradient of the flow field, better conforming to the fluid motion characteristics. Especially in high-gradient regions such as shear layers and vortex cores, it can significantly reduce peak-locking errors and velocity smoothing effects.

[0054] In step S5, an analytical algorithm based on vorticity-temperature gradient coupling is adopted: firstly, the Q criterion is used. The vortex structure is identified, where Ω is the vortex tensor and S is the strain rate tensor; then the temperature gradient distribution in the vortex core region (diameter <2 mm) is extracted, and the local Nusselt number is calculated. Where h is the heat transfer coefficient, d is the characteristic length, and λ is the gas thermal conductivity. For a typical 40Hz vortex shedding process, the dynamic characteristic of the vortex core temperature being 8-12K lower than the surrounding area can be captured with a time resolution of 0.33ms.

[0055] In this embodiment, the analytical algorithm based on vortex-temperature gradient coupling is a quantitative analysis method based on the coupling of vortex identification criteria and thermophysical parameters. It extracts the dynamic correlation between vortex structure and heat transfer characteristics from synchronously acquired velocity field and temperature field data, and realizes high spatiotemporal resolution quantitative analysis of local heat-fluid coupling characteristics during the dynamic evolution of vortices.

[0056] Specifically, firstly through the Q criterion The Q-criteria is used to identify vortex structures, where Ω is the vorticity tensor and S is the strain rate tensor. As a vortex identification method based on the velocity gradient tensor, the Q-criteria can effectively distinguish between rotation-dominated regions (vortex cores) and shear-dominated regions, possessing strong physical basis and noise resistance. Its threshold is set as follows: This highlights the region where the rotational intensity is significantly higher than the strain rate, thereby accurately locating the vortex core.

[0057] Based on this, the temperature gradient distribution in the vortex core region (diameter < 2 mm) was further extracted, and the local Nusselt number was calculated. , where h is the local convective heat transfer coefficient, d is the characteristic length, and λ is the gas thermal conductivity. The calculation of the Nusselt number directly correlates the dynamic characteristics of the flow field with the heat transfer performance, enabling the quantification of the influence of vortices on the heat transfer process.

[0058] To further optimize the technical solution, this embodiment also provides a two-dimensional multimodal turbine blade thermal-fluid dual-field synchronous testing system, including:

[0059] Particle seeding module: used to uniformly seed phosphorescent particles in the experimental flow field of the target turbine blade;

[0060] Optical measurement module: used to acquire flow field images through multiple cameras after the phosphorescent particles are excited by dual lasers;

[0061] Synchronization control module: Used to control the timing synchronization of dual lasers and multiple cameras;

[0062] Data processing module: Based on the flow field image, it uses the phosphorescence intensity ratio method to invert the temperature field and the improved PIV algorithm to calculate the velocity field. After spatiotemporal registration of the temperature field and velocity field data, they are fused to generate a composite thermal-flow field. The dynamic coupling characteristics are analyzed by the vortex core tracking algorithm to complete the synchronous test of the thermal-flow dual field of the target turbine blade.

[0063] Specifically, such as Figure 1 , Figure 2 As shown, the system's modules are structured as follows:

[0064] The particle spreading module uses a reverse cyclone seeder, particle storage tank and flow control system to ensure uniform particle distribution; the particle spreading module includes a centrifugal separation chamber (80mm in diameter), a dual-path temperature control system (278~450K±5K) and a particle concentration monitor.

[0065] In the particle seeding module, the connection method of each device follows the systematic design principle of precise control of gas-solid two-phase flow and thermal environment simulation. Through the organic integration of gas path, material path, and control path, high-precision control of phosphorescent particle concentration, temperature, and spatial distribution is achieved. The specific connection method is as follows:

[0066] Gas path connection and flow control: The gas source is connected to the flow control system via a pressure reducing valve to ensure a stable intake flow rate. The gas flow is then injected tangentially into the centrifuge, forming a stable vortex within the centrifuge, providing the driving force for particle dispersion. The centrifuge outlet is connected to the experimental flow field inlet via a flexible, high-temperature resistant pipeline, forming a unidirectional supply channel.

[0067] Material supply and dispersion path: The particle storage tank is located upstream of the centrifuge and is coupled to the main gas path via a micro-screw feeder. Phosphorescent particles in the tank enter the centrifuge under the influence of the airflow, achieving thorough mixing and deagglomeration of the gas and solid phases within a strong swirling flow field. Particle concentration is fed back in real-time by a particle concentration monitor, and the feed rate is adjusted through closed-loop control to stabilize the particle density at the desired concentration. Temperature control system integration:

[0068] The dual-path temperature control system operates independently in the main and secondary flow channels, employing armored heaters and a PID control array to achieve a temperature control accuracy of ±5K (range 278~450K). Heating elements are embedded in the flow channel walls, and thermocouples are placed at key nodes to monitor the temperature in real time and provide feedback to the PID controller. The temperature control system is connected to the main control unit via an electrical interface to ensure that the flow field temperature distribution can simulate the actual turbine blade operating conditions.

[0069] Control and Monitoring Architecture: The entire module is coordinated by a central control unit. The flow control system, particle concentration monitor, and temperature control system communicate with the host computer via a data acquisition card to achieve parameter setting, real-time monitoring, and closed-loop adjustment. The system has automatic calibration and fault diagnosis functions to ensure stability and repeatability during long-term operation.

[0070] The optical measurement module employs an ultraviolet laser (3kHz@355nm), a PIV laser (3kHz@532nm), four sCMOS cameras (16bit@3000fps), a dichroic mirror, and a cylindrical mirror assembly; the microscope lens is a Tamron 60mm f / 2.0 with a spatial resolution of 40μm.

[0071] The synchronization control module uses a quantum synthesizer and a synchronization delay generator, with a timing error of <10ns; the quantum synthesizer has a timing jitter of <1ns, and the photodiode array has a response time of 1ns.

[0072] The synchronous delay generator controls dual lasers, four sCMOS cameras, and other synchronous sensors. Its core function is to coordinate the strict synchronization of excitation and acquisition, controlling the timing error within <10ns to meet the extreme timing accuracy requirements of high-speed transient process measurements.

[0073] In this module, the photodiode array primarily serves as a high-precision timing verification and feedback monitoring device, rather than a direct control device. It is used for laser pulse timing monitoring and camera exposure synchronization verification feedback signals. Therefore, the photodiode array itself does not directly control other devices; instead, it acts as a high-precision sensor, providing crucial real-time timing verification data for the quantum synthesizer and synchronization delay generator. It is a key monitoring and calibration component ensuring the entire system achieves nanosecond-level synchronization accuracy.

[0074] The data processing module employs a real-time processing unit (delay 0.8s) and analysis software to perform background noise suppression, single-field reconstruction, spatiotemporal registration, and dynamic coupling algorithms. It outputs comprehensive thermal-fluid field visualization results, supports real-time processing (delay ≤1s) and offline analysis, and outputs temperature cloud maps, velocity vector fields, and dynamic maps of composite thermal-fluid fields.

[0075] Performance verification and experimental results:

[0076] In a standard flat-plate turbulent boundary layer experiment (Re=15,000), the temperature field accuracy was ±3.2K (95% confidence interval), and the velocity field uncertainty was 0.28 m / s. Compared with conventional thermocouples, when measuring the peak heat flux in the leading-edge stagnation region, the Nusselt number distribution obtained by this method showed a 93% agreement with the CFD simulation results, while the thermocouple measurement deviation reached 18%. In dynamic testing, the periodic vortex shedding process at the blade trailing edge at 42Hz was successfully captured, with a temperature fluctuation range of ±7K, and the spatial resolution was verified as 38±2μm by the USAF1951 target.

[0077] (1) High-precision full-domain synchronous measurement: Through innovative ultraviolet-excited phosphorescence thermometry and PIV dual-field synchronous technology, synchronous measurement of the temperature field (measurement accuracy ±3.2K) and velocity field (measurement uncertainty <0.3m / s) was achieved, with spatial resolution reaching the micrometer level. This breaks through the limitations of traditional single-point measurement and can simultaneously and accurately capture the thermal-fluid coupling characteristics of key parts such as the leading edge stagnation zone, fin gap, and trailing edge vortex shedding zone of the blade. The measurement coverage can reach the full domain of 50×50mm². Compared with traditional infrared thermometry, the spatial resolution is improved by more than 5 times, providing reliable data support for the refined analysis of blade cooling performance.

[0078] (2) Non-contact dynamic testing: Reverse cyclone particle seeding technology is used to ensure the uniform distribution of tracer particles with particle sizes ranging from nanometer to submicrometer in the flow field (density is stable at 6×10¹¹~5×10¹¹). 12 (particles / m³). By employing a dual-laser coaxial excitation scheme of 355nm and 532nm, the problem of operating condition deviation caused by traditional multiple measurements is avoided, achieving truly synchronous and interference-free measurement.

[0079] (3) Adaptability to extreme environments: The high-temperature resistant phosphorescent material used exhibits excellent stability at 450K, with the temperature measurement standard deviation remaining within ±3.8K after 2 hours of continuous operation. Combined with a 3kHz high-speed acquisition system and μs-level synchronous control, it can accurately analyze transient processes. Even under strong vibration environments, the system can still maintain excellent measurement performance with a signal-to-noise ratio >10:1, fully meeting the testing requirements of the actual working environment of aero-engines.

[0080] (4) In terms of practical engineering output: It can generate temperature-velocity coupled cloud maps and corresponding professional data analysis charts, which greatly improves the convenience of engineering applications.

[0081] (5) In terms of overall performance improvement: the traditional experimental cycle of 3 months is significantly shortened to 3 weeks, and the cost of a single measurement is reduced by 60%. In the actual application of a certain type of turbine blade, these advantages make this technology one of the most groundbreaking technical solutions in the field of turbine blade thermodynamic performance testing.

[0082] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades, characterized in that, include: Phosphorescent particles were uniformly seeded in the experimental flow field of the target turbine blade. After the phosphorescent particles were excited by dual lasers, flow field images were acquired. Based on the flow field image, the temperature field is inverted using the phosphorescence intensity ratio method, and the velocity field is calculated using the improved PIV algorithm. After spatiotemporal registration of temperature and velocity field data, they are fused to generate a composite thermal-fluid field. The dynamic coupling characteristics are then analyzed using a vortex core tracking algorithm to complete the synchronous test of the target turbine blade's thermal-fluid dual fields.

2. The method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades according to claim 1, characterized in that, The seeding of phosphorescent particles within the experimental flow field of the target turbine blade includes: Phosphorescent particles with a particle size of nanometer to submicron were uniformly dispersed into the experimental flow field using a reverse cyclone seeding system, wherein the particle density was controlled at 6×10¹¹ to 5×10¹¹. 12 particles / m³.

3. The method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades according to claim 1, characterized in that, Excitation of the phosphorescent particles by dual lasers includes: The phosphorescent particles are excited by a time-division multiplexing technique using an ultraviolet laser and an Nd:YAG laser. The ultraviolet laser is used to excite the temperature-sensitive phosphorescent signal of the phosphorescent particles, and the Nd:YAG laser is used for PIV velocimetry.

4. The method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades according to claim 1, characterized in that, Acquiring the flow field images includes: Four cameras were used to simultaneously acquire flow field images. Two cameras were equipped with filters of 387±11nm and 425±50nm respectively for temperature field measurement, and the other two cameras were equipped with filters of 532±10nm for velocity field measurement.

5. The method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades according to claim 1, characterized in that, The temperature field inversion method using phosphorescence intensity ratio includes: After performing non-uniformity correction on the dual-channel image, the intensity ratio of phosphorescence in the dual-channel image is calculated; Based on the intensity ratio, the temperature is retrieved using a pre-calibrated inversion model, which is: T=-23.6R³+148.3R²+265.7R+18.4; Where T is temperature and R is phosphorescence intensity ratio.

6. The method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades according to claim 1, characterized in that, The velocity field is calculated using the improved PIV algorithm, including: The first frame uses a 64×64 pixel window for initial cross-correlation calculation, and subsequent frames use a 32×32 pixel adaptive deformable mesh, combined with particle trajectory prediction technology for calculation speed.

7. The method for simultaneous thermal-fluid dual-field testing of two-dimensional multimodal turbine blades according to claim 1, characterized in that, The dynamic coupling characteristics are analyzed using the vortex core tracking algorithm, including: Based on velocity field data, the location of the vortex core is identified by the local velocity gradient tensor, and the temperature field data is correlated to calculate the local Nusselt number, thereby analyzing the transient thermal-fluid coupling characteristics of the vortex shedding process.

8. A two-dimensional multimodal turbine blade thermal-fluid dual-field synchronous testing system, characterized in that, include: Particle seeding module: used to uniformly seed phosphorescent particles in the experimental flow field of the target turbine blade; Optical measurement module: used to acquire flow field images through multiple cameras after the phosphorescent particles are excited by dual lasers; Synchronization control module: Used to control the timing synchronization of dual lasers and multiple cameras; Data processing module: Based on the flow field image, it uses the phosphorescence intensity ratio method to invert the temperature field and the improved PIV algorithm to calculate the velocity field. After spatiotemporal registration of the temperature field and velocity field data, they are fused to generate a composite thermal-flow field. The dynamic coupling characteristics are analyzed by the vortex core tracking algorithm to complete the synchronous test of the thermal-flow dual field of the target turbine blade.

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