A method and system for constructing a high-temperature sound field mapping of a sootblower lance

Abstract

This invention discloses a method and system for constructing a high-temperature acoustic field mapping for sootblower barrels, belonging to the field of non-destructive testing technology. The method includes: constructing a simulation model incorporating microstructural features to simulate the propagation behavior of sound waves in a target material; acquiring acoustic response data under a high-temperature test environment; and generating a mapping relationship between temperature, medium, and acoustic field parameters based on the comparison between the simulation model output and the acoustic response data. This invention controls the sound velocity prediction deviation to within 2%, enabling real-time monitoring of defects such as barrel wall thinning and internal cracks under high-temperature environments above 400℃.

Description

Technical Field

[0001] This invention belongs to the field of nondestructive testing technology, specifically relating to a method and system for constructing high-temperature sound field mapping for sootblower barrels. Background Technology

[0002] In industrial equipment such as coal-fired boilers, high-temperature metal pipelines operate in harsh environments for extended periods. Take long-range sootblower barrels as an example: these can reach lengths of up to 16 meters and have an outer diameter of approximately 140 millimeters. They are constantly exposed to furnace temperatures exceeding 400°C, while simultaneously enduring the continuous scouring of high-velocity steam media and the complex mechanical and alternating thermal stresses generated during rotation and expansion. These complex service conditions make sootblower barrels highly susceptible to failures such as thinning due to blown-through, thermal fatigue fracture of welds, and ash leakage from the nozzle sealing area. Reliable online detection and monitoring of high-temperature metal pipelines is crucial for ensuring safe equipment operation and preventing unplanned shutdowns.

[0003] However, current acoustic testing technology for high-temperature metal pipes faces the following fundamental challenges: First, existing detection methods suffer from serious timeliness and blind spots. Traditional methods such as ultrasonic thickness measurement and magnetic particle testing require shutdown and cannot be performed in real time while the pipeline is in operation. For slender barrels over ten meters long, point-by-point manual inspection is not only extremely inefficient and time-consuming, but also requires inspectors to enter hazardous areas, posing a high safety risk. More importantly, this discrete, shutdown-based inspection can only detect mature defects, failing to provide timely warnings for early-stage defects that are emerging or spreading, resulting in a significant delay in information feedback and substantial blind spots in safety monitoring.

[0004] Secondly, the propagation law of ultrasound changes fundamentally under high-temperature conditions, which existing models cannot accurately describe. At temperatures above 400℃, the physical properties of pipe materials change significantly: on the one hand, parameters such as the material's elastic constants and acoustic impedance drift nonlinearly with temperature, rendering the sound velocity-wall thickness conversion formula established at room temperature completely invalid; on the other hand, metallic materials undergo microstructural aging during long-term high-temperature operation, manifested as grain coarsening, carbide precipitation, and grain boundary evolution. These microstructural changes significantly enhance the scattering effect of sound waves at grain boundaries, causing distortion of the sound field distribution and exacerbated energy attenuation. However, existing sound field simulation models generally adopt the assumption of macroscopic homogeneity, completely ignoring the influence of microscopic grain structure on sound wave propagation and treating the material as a continuous homogeneous medium. This simplification is acceptable at room temperature, but at high temperatures, where grain boundary scattering becomes the dominant factor, the deviation between simulation results and measured data can reach more than 10%, failing to provide reliable benchmark parameters for online detection.

[0005] Third, the mapping relationship between temperature, medium, and acoustic field parameters is completely missing. The actual operating conditions of the sootblower barrel are dynamic: the furnace temperature fluctuates under different loads (400~650℃), the steam velocity varies with the sootblowing cycle (0~50m / s), and the medium pressure also fluctuates periodically. Under these dynamic conditions, the variation patterns of key acoustic parameters such as sound velocity and attenuation coefficient with temperature and medium conditions are unclear, lacking a systematic quantitative description. This prevents the online detection system from automatically adjusting detection parameters and criteria according to real-time operating conditions. The same echo signal may indicate normal wall thickness at 400℃, but may indicate severe thinning at 600℃. This uncertainty makes it difficult to stably analyze and accurately interpret the detection results.

[0006] In summary, the core deficiency of existing technologies lies in the difficulty of accurately obtaining the propagation laws of sound fields under high-temperature environments, and the lack of a mapping relationship between temperature, medium, and sound field parameters. This unresolved fundamental scientific problem directly restricts the development of online high-temperature detection technology for sootblower barrels, forcing current reliance on inefficient, delayed, and high-risk shutdown detection methods. Therefore, a method capable of systematically establishing high-temperature sound field mapping relationships is urgently needed to provide a theoretical foundation and data support for online detection. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for constructing high-temperature sound field mapping of sootblower barrels, which addresses the shortcomings of the prior art and provides a basic support for accurate mapping and online detection of sound field parameters under high-temperature conditions. This is used to solve the technical problem that it is currently impossible to obtain the sound field propagation law in real time and accurately under high-temperature operating conditions.

[0008] The present invention adopts the following technical solution: A method for constructing a high-temperature sound field mapping for a sootblower barrel includes the following steps: S1. Construct a simulation model that includes microstructure characteristics, wherein the simulation model is configured to simulate the propagation behavior of sound waves in the target material; S2. Acquire acoustic response data under high-temperature test conditions; S3. Based on the comparison between the output of the simulation model and the acoustic response data, generate the mapping relationship between temperature, medium and sound field parameters.

[0009] Preferably, the construction of the simulation model including microscopic organizational features includes: A geometric model is constructed based on the grain size distribution function and elastic constant matrix of the material; multiphysics coupling boundary conditions are set for the geometric model.

[0010] Preferably, the construction of the geometric model based on the material's grain size distribution function and elastic constant matrix includes: The grain geometry topology is generated using the Voronoi polygon algorithm; based on the grain geometry topology, the wave equation is solved using the finite element method to obtain the sound field distribution data.

[0011] Preferably, acquiring acoustic response data under a high-temperature test environment includes: A high-temperature testing platform is provided, which includes a heating chamber, a sample fixing unit, an ultrasonic transducer, and a data acquisition module. Under different temperature conditions and medium flow parameters, the ultrasonic transducer collects sound field signals, and the data acquisition module obtains the acoustic response data.

[0012] Preferably, after acquiring the acoustic response data through the data acquisition module, the method further includes: The sound field signal is subjected to noise reduction processing; acoustic feature parameters are extracted from the noise-reduced sound field signal, the acoustic feature parameters including sound velocity and attenuation coefficient.

[0013] Preferably, the step of generating a mapping relationship between temperature, medium, and sound field parameters based on the comparison results between the output of the simulation model and the acoustic response data includes: Based on the acoustic response data, the material parameters or boundary conditions of the simulation model are corrected; based on the corrected simulation model, a mapping dataset between temperature, medium and sound field parameters is generated.

[0014] Preferably, the dataset generating the mapping between temperature, medium, and sound field parameters includes: The mapping dataset is fitted using regression analysis or neural network algorithms to obtain a mapping function; or a multidimensional mapping database is constructed, which stores the correspondence between temperature, medium, and sound field parameters.

[0015] Preferably, the high-temperature metal pipe is a sootblower barrel, which is configured to operate in a high-temperature environment above 400°C and withstand the scouring of steam medium.

[0016] Preferably, the microstructure features include grain size distribution and grain boundary orientation difference distribution.

[0017] Secondly, embodiments of the present invention provide a high-temperature sound field mapping construction system for sootblower barrels, comprising: The model building module is configured to build a simulation model containing microstructure features, which is used to simulate the propagation behavior of sound waves in the target material; The test data acquisition module is configured to acquire acoustic response data under high-temperature test conditions. The mapping relationship generation module is configured to generate a mapping relationship between temperature, medium, and sound field parameters based on the comparison results between the output of the simulation model and the acoustic response data.

[0018] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for constructing high-temperature sound field mapping of sootblower barrels.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for constructing high-temperature sound field mapping of a sootblower barrel.

[0020] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for constructing high-temperature sound field mapping of a sootblower barrel.

[0021] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for constructing high-temperature sound field mapping of sootblower barrels.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects: A method for constructing a high-temperature sound field mapping for sootblower barrels systematically solves the problem of accurately obtaining the propagation law of high-temperature sound fields in sootblower barrels through a three-step collaborative approach: simulation modeling, experimental data acquisition, and comparative mapping. First, a microstructure simulation model incorporating grain size distribution and grain boundary orientation difference distribution is constructed, breaking through the traditional homogeneous assumption and physically reproducing the grain boundary scattering effect at high temperatures. This reduces the sound velocity prediction deviation at 600℃ from 12.6% in the traditional model to less than 2%. Second, a high-temperature experimental platform is built to realistically acquire acoustic response data, and wavelet threshold noise reduction is applied, improving the sound velocity measurement repeatability from ±3% to ±0.5% and the attenuation coefficient measurement accuracy by more than 60%, providing a reliable calibration benchmark for simulation. Finally, the simulation output is compared with the experimental data, and a closed-loop correction is used to generate the mapping relationship between temperature, medium, and sound field parameters. This allows the online detection system to obtain the theoretical sound velocity in milliseconds based on real-time operating conditions, eliminating the need to rerun complex simulations. The three technologies work together to reduce the detection response time from several hours after shutdown to less than 1 second, enabling continuous real-time monitoring of the sootblower barrel under high-temperature operation, and providing a reliable theoretical basis and data support for early warning of wall thickness reduction and crack propagation.

[0023] Furthermore, by constructing a geometric model based on the material's grain size distribution function and elastic constant matrix, and setting multi-physics coupling boundary conditions, sound field simulation based on the microscopic nature of the material is achieved. The change in sound wave propagation characteristics at high temperatures mainly stems from the enhanced grain boundary scattering effect. Traditional homogeneous models cannot capture this physical mechanism, while the microscopic modeling method of this invention directly reconstructs the physical process of sound wave interaction with the material at the grain scale. This improves the accuracy of sound velocity prediction under high-temperature conditions from a deviation of >10% in traditional models to a deviation of <3%.

[0024] Furthermore, the Voronoi polygon algorithm is used to generate the grain geometry and topology, and combined with the finite element method to solve the wave equation. The Voronoi algorithm can realistically simulate the random grain morphology of polycrystalline materials, and the generated grain size distribution conforms to the log-normal distribution law, which is highly consistent with the real metallographic structure. The finite element method can accurately calculate the reflection, refraction, and mode conversion of sound waves at grain boundaries. The combination of the two enables the simulation model to physically reproduce the scattering behavior of sound waves at grain boundaries. Compared with the traditional homogeneous model, the simulation accuracy of the sound field distribution data can be improved by more than 40% under conditions above 400℃.

[0025] Furthermore, the high-temperature testing platform includes a heating chamber, a sample fixing unit, an ultrasonic transducer, and a data acquisition module. It can achieve precise temperature control within a range of room temperature to 800℃ and simulate different medium flow rate conditions. It solves the problem of lacking realistic boundary condition calibration in simple simulations by acquiring measured data under a controlled environment, providing a reliable truth benchmark for the simulation model and ensuring the engineering authenticity of the mapping relationship.

[0026] Furthermore, wavelet thresholding denoising was employed to process the original sound field signal, and sound velocity and attenuation coefficient were extracted as key feature parameters. The spectral characteristics of thermal noise, turbulence noise, and electromagnetic interference differ from the effective sound field signal under high-temperature conditions. Wavelet transform can decompose the signal into different frequency bands, and thresholding effectively suppresses noise components. After processing, the repeatability of sound velocity measurement improved from ±3% to ±0.5%, and the accuracy of attenuation coefficient measurement improved by more than 60%.

[0027] Furthermore, by utilizing a limited number of experimental data points, key parameters in the simulation model were calibrated in reverse, enabling the corrected model to approximate the real physical field through iterative convergence. After 2-3 iterations of correction, the sound velocity prediction deviation of the simulation model at the experimental points could be reduced to within 2%, achieving high-precision calibration of the model.

[0028] Furthermore, the mapping function form enables the online detection system to calculate the theoretical sound field parameters under the current operating conditions in real time without complex simulations, reducing the calculation response time from hours to milliseconds; the multidimensional mapping database form supports fast retrieval and interpolation calls in discrete operating conditions, with a query response time of less than 10ms. Both forms together ensure the real-time performance and accuracy of the mapping relationships in engineering applications.

[0029] Furthermore, it can provide accurate reference parameters for online ultrasonic testing systems, enabling real-time monitoring of defects such as barrel wall thinning and internal cracks without stopping or disassembling the system. This shortens the detection response time from several hours after shutdown to real-time acquisition during operation, solving the fundamental problem of online inspection of long-range sootblower barrels.

[0030] Furthermore, the microstructure characteristics are specifically defined as grain size distribution and grain boundary orientation difference distribution. Grain size distribution determines the mean free path of sound wave scattering, while grain boundary orientation difference distribution determines the energy distribution ratio of sound waves at grain boundaries. By characterizing and modeling these two parameters, the simulation model can accurately reproduce the acoustic response differences of materials under different heat treatment states, making the model applicable to the entire life cycle of materials.

[0031] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0032] In summary, this invention generates a high-temperature sound field mapping relationship through microscopic simulation and experimental closed-loop correction, reducing the sound velocity prediction deviation to within 2%, shortening the detection response from several hours to 1 second, realizing online real-time monitoring of the sootblower barrel, effectively warning of defects, reducing operation and maintenance risks and costs, and significantly improving equipment safety and economy.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] Figure 1 This is a flowchart of the construction method of the present invention; Figure 2 This is a schematic diagram of the high-temperature test platform structure according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the construction principle of a simulation model containing microstructural features according to an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the logic for generating the mapping relationship between temperature, medium, and sound field parameters in an embodiment of the present invention. Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 6This is a block diagram of a chip provided according to an embodiment of the present invention.

[0035] The components include: 1. Heating chamber; 2. Heating element; 3. Temperature sensor; 4. Pipe sample fixing unit; 5. High-temperature metal pipe sample; 6. Ultrasonic transducer; 7. Medium inflow interface; 8. Medium outflow interface; 9. Signal conditioning unit; 10. Data acquisition module; 11. Temperature and flow control unit; 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic equipment; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0036] 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, not all, of the embodiments of the present invention. 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.

[0037] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0038] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0039] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0040] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0041] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0042] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0043] This invention provides a method for constructing a high-temperature sound field mapping for a sootblower barrel, aiming to solve the problems in the existing technology, such as the difficulty in accurately obtaining the sound field propagation law under high-temperature environment, the need to stop the machine for detection and the existence of blind spots, the large prediction deviation caused by the neglect of grain boundary scattering in the traditional macroscopic homogeneous simulation model (sound velocity deviation > 10% at 600℃), and the lack of quantitative mapping relationship between temperature-medium-sound field, resulting in a lack of reference parameters for online detection.

[0044] Compared with the prior art, the core improvement of this invention lies in: First, it breaks through the traditional homogeneous assumption and constructs a microstructure simulation model that includes grain size distribution and grain boundary orientation difference distribution. It uses the Voronoi algorithm and finite element method to restore the high-temperature grain boundary scattering effect from a physical mechanism, reducing the sound velocity prediction error to less than 2%. Secondly, a controllable high-temperature test platform (room temperature to 800℃, temperature control accuracy ±2℃) was built to obtain real acoustic response data. After wavelet denoising, high-precision feature parameters were extracted to provide a reliable calibration benchmark for simulation. Third, a simulation-experiment closed-loop correction mechanism is established. The simulation model is calibrated based on a limited number of experimental data points, and finally, a temperature-medium-sound field mapping function or database that can be used in engineering is generated, enabling the online detection system to obtain theoretical sound field parameters in milliseconds based on real-time operating conditions.

[0045] The above improvements enable continuous online monitoring of the sootblower barrel under high temperature and steam scouring conditions above 400°C. The detection response time is reduced from several hours after shutdown to less than 1 second, and the defect warning lead time reaches 23 days. This effectively solves the problem of blind spots in safety monitoring using traditional methods and significantly improves the reliability and economy of equipment operation.

[0046] Example 1 Please see Figure 1 The present invention provides a method for constructing a high-temperature sound field mapping for a sootblower barrel, comprising the following steps: S1. Construct a simulation model that includes microstructure characteristics, and configure the simulation model to simulate the propagation behavior of sound waves in the target material; Microstructure characteristics refer to the microscopic geometric and physical properties of metallic materials, such as grain structure, grain boundary distribution, and phase composition. In high-temperature environments, the scattering and attenuation behavior of sound waves is primarily controlled by the coupling effect between the material's microstructure and the temperature field. Traditional homogeneous models often neglect grain boundary scattering effects, leading to significant discrepancies between simulation results and actual conditions.

[0047] This embodiment introduces microstructural features to construct a numerical model that reflects the inherent acoustic properties of materials. The model's input parameters typically include the statistical distribution of grain size, the elastic constant matrix, and thermophysical parameters at high temperatures. Its output is sound field distribution data, such as the propagation path, sound pressure distribution, and attenuation characteristics of sound waves at a specific temperature. While this embodiment focuses on modeling the microstructure, in other implementations, the simulation model can further incorporate macroscopic geometric defects, such as cracks or pores, to expand its applicability.

[0048] S2. Acquire acoustic response data under high-temperature test conditions; This step involves constructing a high-temperature test platform capable of simulating actual working conditions to acquire acoustic signals under real-world conditions. While simulation models can obtain full-field sound distribution data, their accuracy is highly dependent on the setting of boundary conditions, which are often difficult to precisely preset at high temperatures. Therefore, it is necessary to obtain acoustic response data at specific temperature points and under specific medium conditions through experimental methods as a calibration benchmark. This acoustic response data mainly includes information such as the waveform, amplitude, phase, and time of flight of the ultrasonic echo signal.

[0049] When performing this step, it is necessary to control parameters such as the temperature gradient and medium flow rate of the test environment to simulate the actual operating conditions of high-temperature metal pipes such as sootblower barrels. For example, multiple temperature test points can be set, such as 400℃, 500℃, 600℃, etc., to collect acoustic signals at different temperatures.

[0050] S3. Based on the comparison results between the output of the simulation model and the acoustic response data, a mapping relationship between temperature, medium and sound field parameters is generated.

[0051] This step is the core of establishing the mapping relationship; its essence is to establish a closed-loop correction mechanism between simulation and experiment. The system compares and analyzes the experimental data obtained in step S2 with the simulation output in step S1, calculating the deviations between the two in key acoustic parameters such as sound velocity and attenuation coefficient.

[0052] When the deviation exceeds a preset threshold, the material parameters (such as the coefficient of elastic modulus with temperature) or boundary conditions in the simulation model are adjusted in reverse until the simulation results match the experimental data within the allowable error range. Based on the corrected high-fidelity simulation model, the system can perform numerical extrapolation over a wider range of temperatures and media, thereby generating a mapping relationship covering all operating conditions. This mapping relationship can be expressed as a data table, a fitted curve, or a function model, intuitively reflecting the influence of temperature changes and media flow on the sound field parameters.

[0053] Through the above solution, this embodiment effectively overcomes the dual limitations of simple simulation lacking accuracy of boundary conditions and simple experiment making it difficult to obtain full-field data, and achieves the complementary advantages of simulation calculation efficiency and experimental data reliability.

[0054] In terms of effectiveness, the closed-loop correction mechanism of this embodiment can converge the prediction deviation of the sound field parameters to within a preset threshold (e.g., the sound velocity deviation is less than 2%). At the same time, based on the corrected high-fidelity simulation model, numerical extrapolation can be performed to expand the range of applicable working conditions from limited discrete test temperature points to continuous global temperature and medium conditions. Compared with traditional schemes that rely solely on homogeneous simulation models or discrete test points, the accuracy and applicability of the mapping relationship are significantly improved, providing a solid theoretical basis and data support for the online detection of high-temperature metal pipelines.

[0055] This invention constructs a simulation-experiment closed-loop correction mechanism. Step S1 establishes a microstructure simulation model and outputs theoretical acoustic field data; Step S2 obtains real acoustic response data through a high-temperature test platform; Step S3 compares the two, calculates the deviation, and when the deviation exceeds 2%, adjusts the material parameters (elastic modulus temperature coefficient, grain boundary scattering coefficient) or boundary conditions (acoustic impedance) of the simulation model in reverse, and converges the deviation through iterative calculation.

[0056] Experiments have verified that, after adopting the closed-loop correction method of this invention: The initial simulation model had a sound velocity prediction error of 12.6% at 600℃. After the first correction, the error decreased to 5.8%; after the second correction, the error decreased to 2.1%; and after the third correction, the error converged to 1.7%, meeting the requirements for engineering applications (<2%). Furthermore, the corrected model can extrapolate and calculate sound field parameters under continuous temperature fields (400℃~650℃) and continuous medium flow velocities (0~50m / s), expanding the operating conditions coverage from three discrete test points to the entire continuous space.

[0057] Example 2 This embodiment, based on Embodiment 1, provides a detailed explanation of the specific process for constructing a simulation model incorporating microstructural features. Traditional sound field simulations typically assume the material is a homogeneous medium. However, under high-temperature conditions, the grain structure of metallic materials significantly enhances the scattering effect of sound waves, leading to distortion of the sound field distribution. Homogeneous models struggle to accurately reflect this physical phenomenon. This embodiment introduces microscopic grain features to construct a high-fidelity simulation model.

[0058] Specifically, a simulation model incorporating microstructure features is constructed, including: building a geometric model based on the grain size distribution function and elastic constant matrix of the material; and setting multiphysics coupling boundary conditions for the geometric model.

[0059] The grain size distribution function describes the statistical regularity of grain size within the material. In high-temperature metal pipe materials, grain size typically follows a log-normal or normal distribution. For example, the average grain diameter can be set to 50 micrometers, and the distribution variance to 10 micrometers, thus characterizing the microstructure of the material. The elastic constant matrix reflects the mechanical response characteristics of the grains in different directions, typically including stiffness coefficients C11, C12, and C44. When sound waves propagate between grains with different orientations, scattering, reflection, and mode switching occur at grain boundaries due to differences in elastic constants; this is the root cause of the complexity of high-temperature sound fields. By incorporating these parameters into the model, the propagation behavior of sound waves can be reconstructed from a physical mechanism perspective.

[0060] Furthermore, based on the grain size distribution function and elastic constant matrix of the material, a geometric model is constructed, including: generating the grain geometric topology using the Voronoi polygon algorithm; and solving the wave equation using the finite element method based on the grain geometric topology to obtain the sound field distribution data.

[0061] The Voronoi polygon algorithm is an efficient method for constructing stochastic geometric models. The specific process is as follows: First, a certain number of seed points are randomly scattered within the computational domain. The density of the seed points is determined based on the grain size distribution function. Then, the distance from each pixel to all seed points is calculated, and the pixel is assigned to the region formed by the nearest seed point, thereby generating several non-overlapping polygonal regions, each polygon representing a grain.

[0062] By adjusting the distribution density and randomness of seed points, the average size and uniformity of grains can be controlled, thereby simulating the microstructure of materials under different heat treatment conditions.

[0063] Although the Voronoi algorithm is preferred in this embodiment, similar grain topologies can also be generated using the Monte Carlo method or the phase-field method in other embodiments, as long as they can reflect the randomness of the material's microstructure.

[0064] After generating the grain geometry and topology, the wave equation is solved using the finite element method. The specific process includes: The geometric model is meshed, typically with finer meshing near grain boundaries to improve computational accuracy. Multiphysics coupling boundary conditions are set, such as acoustic impedance boundary conditions on the model surface to simulate sound wave emission and reception, while temperature loads are introduced to simulate the effect of high-temperature environments on the material's elastic constants. By solving the wave equation, sound field distribution data such as the propagation path, sound pressure distribution, and scattering attenuation coefficient of the sound wave within the microcrystalline structure can be obtained.

[0065] This embodiment successfully simulates the scattering effect of sound waves at grain boundaries at high temperatures using the aforementioned microscopic modeling method, overcoming the shortcomings of traditional homogeneous models in simulating high-temperature sound fields. In terms of performance, by introducing the grain size distribution function and the elastic constant matrix, the simulation model can physically reconstruct the scattering and mode conversion behavior of sound waves at grain boundaries. Compared to traditional homogeneous models that ignore microstructure characteristics, the simulation accuracy of sound field distribution data is significantly improved. Especially under high-temperature conditions above 400℃, the predicted results for sound velocity and attenuation coefficient are closer to real experimental data, laying a theoretical foundation for subsequently generating high-precision mapping relationships.

[0066] This embodiment uses the Voronoi polygon algorithm to generate the grain geometry and topology, setting the average grain diameter to 50 μm and the variance to 10 μm, which conforms to the statistical laws of the actual metallographic structure of the material. Based on this, the finite element method is used to solve the wave equation, and abrupt boundary conditions of elastic constant change are set at the grain boundaries to simulate the reflection, refraction, and mode conversion of sound waves at the grain boundaries. Comparative experiments on the same material (heat-resistant alloy steel) show that: Under 500℃ conditions, the traditional homogeneous model predicted a sound velocity of 2850 m / s, while the measured value was 3120 m / s, a deviation of 8.7%. The microscopic model of this invention predicted a sound velocity of 3105 m / s, with a deviation of only 0.48%. Regarding the attenuation coefficient, the traditional homogeneous model predicted a value of 0.12 dB / mm, while the measured value was 0.35 dB / mm, a deviation of 65.7%. The microscopic model of this invention predicted a value of 0.34 dB / mm, with a deviation of 2.86%. Simulation efficiency: Using a high-performance workstation (12-core CPU, 64GB memory), a single simulation takes approximately 45 minutes, reducing the cost of grain characterization by more than 90% compared to electron microscopy experiments.

[0067] Example 3 This embodiment, based on Embodiment 1, provides a detailed explanation of the specific process for acquiring acoustic response data under high-temperature testing conditions. While simulation models can theoretically simulate sound field propagation, their accuracy is highly dependent on the realism of the boundary conditions. To obtain reliable acoustic response data for calibrating the simulation model, a testing platform capable of simulating actual high-temperature conditions must be constructed.

[0068] Specifically, acquiring acoustic response data under high-temperature testing conditions includes: providing a high-temperature testing platform, which includes a heating chamber, a sample fixing unit, an ultrasonic transducer, and a data acquisition module; acquiring sound field signals through the ultrasonic transducer under different temperature conditions and medium flow parameters, and acquiring acoustic response data through the data acquisition module.

[0069] Please see Figure 2 The high-temperature test platform is the core hardware carrier for acquiring test data in this embodiment. Among them, the heating chamber 1 is a sealed insulated chamber, which is equipped with heating elements 2 and temperature sensors 3. It can provide a stable high-temperature test environment, with a temperature control range covering room temperature to 800℃. The temperature control accuracy is preferably ±2℃ to ensure the uniformity and stability of the temperature field inside the chamber.

[0070] It should be understood that the heating element 2 can adopt various forms such as resistance wire heating, silicon carbide rod heating, or electromagnetic induction heating, as long as it can meet the power and temperature requirements of high-temperature testing; the temperature sensor 3 collects the temperature data in the cavity in real time and feeds it back to the temperature control and flow control unit 11 to achieve closed-loop temperature control. The temperature control and flow control unit 11 is connected to the heating element 2, the temperature sensor 3, and the medium inflow interface 7 and outflow interface 8 respectively, and can synchronously adjust the cavity temperature and medium flow rate to simulate the coupling environment of temperature field and flow field under actual working conditions; the pipe sample fixing unit 4 is set at the outlet end of the heating cavity 1 and communicates with the cavity interior, and is used to horizontally clamp the high-temperature metal pipe sample 5 to be tested. The fixing unit is designed with an adjustable structure to adapt to pipe samples with different outer diameters (such as 60~200mm) and different lengths (such as 0.5~2m); at the same time, the fixing unit adopts a clamping structure with elastic compensation, which can offset the thermal expansion displacement of the sample at high temperature, resist the vibration interference caused by medium scouring, avoid sample position displacement during testing, and ensure the positional accuracy of ultrasonic testing. The medium inlet 7 is connected to one end of the pipe sample 5, and the medium outlet 8 is connected to the other end of the sample, forming a complete medium circulation channel. High-temperature steam, flue gas, or other media simulating actual working conditions can be introduced through this channel to achieve test condition settings for different medium types, flow rates, and temperatures, providing support for obtaining acoustic response data under real working conditions. The ultrasonic transducer 6, as the core component for sound wave transmission and reception, is arranged at the preset test position of the pipe sample 5. Considering the damage to the probe's piezoelectric crystal caused by high-temperature environments, this embodiment preferably uses an external high-temperature resistant ultrasonic probe, coupled to the outer wall of the sample via a waveguide rod or high-temperature coupling agent; in other embodiments, laser ultrasonic technology can also be used to achieve non-contact excitation and reception, further improving the reliability of testing under high-temperature environments. Furthermore, multiple sets of ultrasonic transducers can be arranged according to test requirements to achieve sound field signal acquisition at different positions and directions. The output end of the ultrasonic transducer 6 is sequentially connected to the signal conditioning unit 9 and the data acquisition module 10. The signal conditioning unit 9 is used to amplify, filter and reduce noise of the original ultrasonic signal output by the transducer to eliminate environmental noise and circuit interference; the data acquisition module 10 is configured to acquire and store the processed sound field response data at high speed to ensure the integrity of the details of the original signal and provide reliable experimental basis data for subsequent simulation data comparison and mapping relationship construction.

[0071] When this platform is working, the cavity and medium are first heated to the preset temperature by the heating and temperature control module, the target medium is introduced and the flow rate is adjusted to the set value; after the working conditions are stable, the ultrasonic transducer emits and receives sound wave signals, which are processed by the signal conditioning unit and stored by the data acquisition module to complete the acquisition of acoustic response data under different working conditions.

[0072] During the experiment, to comprehensively cover the actual operating conditions of high-temperature metal pipelines, different temperature conditions and media flow parameters need to be set. For example, temperature conditions can be set to multiple gradients such as 400℃, 500℃, and 600℃ to simulate the acoustic field characteristics of the sootblower barrel at different furnace temperatures. Media flow parameters involve the type of medium (e.g., steam, air, or water) and flow velocity (e.g., 10m / s, 20m / s, 30m / s). By controlling the temperature of the heating chamber and introducing the flowing medium, the complex acoustic environment of the pipeline under high temperature and media scouring can be simulated. At each set operating point, the ultrasonic transducer emits ultrasonic pulses to the pipeline sample and receives the echo signals after propagation through the pipeline material. The data acquisition module records these raw waveform data containing acoustic field information at a high sampling rate (e.g., 100MHz).

[0073] Furthermore, after acquiring acoustic response data through the data acquisition module, the process also includes: noise reduction of the sound field signal; and extraction of acoustic feature parameters from the noise-reduced sound field signal, including sound velocity and attenuation coefficient.

[0074] Due to thermal noise in high-temperature environments, turbulence noise in flowing media, and electromagnetic interference from heating equipment, the original sound field signal often contains a large amount of noise components, and directly extracting feature parameters will lead to significant errors. Therefore, noise reduction processing of the sound field signal is necessary. This embodiment preferably employs a wavelet thresholding noise reduction method. The specific process is as follows: First, the original sound field signal is decomposed into multi-scale wavelet components to obtain coefficients for different frequency sub-bands; then, a threshold is set according to the noise level, and coefficients below the threshold are set to zero, while signal coefficients above the threshold are retained; finally, the processed coefficients are used to reconstruct the signal, resulting in the denoised sound field signal. In other embodiments, empirical mode decomposition (EMD) or adaptive filtering methods can also be used for noise reduction.

[0075] After obtaining a high-quality noise-reduced signal, the system extracts acoustic feature parameters from the noise-reduced sound field signal. The sound velocity can be calculated by measuring the propagation time of the ultrasonic wave within a known length of the pipe sample, using the following formula: ,in, For the speed of sound, For the propagation path length, For the duration of transmission.

[0076] The attenuation coefficient reflects the energy loss of sound waves propagating in a material. It can be calculated by comparing the echo amplitude at different distances, using the following formula: ,in, The attenuation coefficient is... and The echo amplitudes are from two different locations. This represents the distance between the two locations.

[0077] These sound velocity and attenuation coefficient data constitute the key acoustic response data used to correct the simulation model. This embodiment ensures the authenticity and accuracy of the experimental data through the aforementioned hardware setup and signal processing flow. In terms of performance, the high-temperature test platform can achieve precise temperature control within the range of room temperature to 800℃ (temperature control accuracy ±2℃). The data acquisition module records the original waveform data at a high sampling rate of 100MHz. Combined with wavelet threshold noise reduction processing, it effectively suppresses thermal noise, turbulence noise, and electromagnetic interference under high-temperature conditions, significantly improving the signal-to-noise ratio of extracted characteristic parameters such as sound velocity and attenuation coefficient. This lays a solid data foundation for subsequently constructing a high-precision mapping relationship.

[0078] The high-temperature test platform built in this embodiment (such as...) Figure 2 The system (as shown) includes: a heating chamber (room temperature ~ 800℃, temperature control accuracy ±2℃), a sample fixing unit (compatible with pipes of outer diameter 60~200mm), an ultrasonic transducer (center frequency 5MHz), and a data acquisition module (sampling rate 100MHz). The original signal was processed using wavelet threshold denoising: the db4 wavelet basis function was selected, decomposed into 5 layers, and soft thresholding was applied. Sound velocity and attenuation coefficient were extracted from the denoised signal. After wavelet threshold denoising, the signal-to-noise ratio was improved by 15-20dB, the repeatability of sound velocity measurement improved from over ±3% to ±0.5%, and the accuracy of attenuation coefficient measurement improved by over 60%, providing high-quality reference data for subsequent model correction.

[0079] Example 4 This embodiment, based on any one of embodiments 1 to 3, provides a detailed explanation of the specific process for generating the mapping relationship between temperature, medium, and sound field parameters. In the prior art, on the one hand, the initial boundary conditions of the simulation model are based on theoretical assumptions, which deviate from the actual experimental environment, and the directly output simulation data cannot completely match the experimental data; on the other hand, the influence of temperature and medium conditions on sound field parameters lacks a systematic quantitative description, causing the online detection system to be unable to automatically adjust the detection parameters and criteria according to real-time operating conditions. This embodiment addresses the above problems by establishing an interactive correction mechanism between the simulation model and experimental data, ensuring that the final output mapping relationship can truly reflect the sound field propagation law under high-temperature and complex operating conditions.

[0080] Specifically, based on the comparison between the output of the simulation model and the acoustic response data, a mapping relationship between temperature, medium and sound field parameters is generated, including: correcting the material parameters or boundary conditions of the simulation model based on the acoustic response data; and generating a mapping dataset between temperature, medium and sound field parameters based on the corrected simulation model.

[0081] like Figure 4As shown, in step S3, the system first compares the high-temperature acoustic response data (such as sound velocity and attenuation coefficient) obtained in Example 3 with the output results of the simulation model in Example 2 under the same working conditions. Since the initial boundary conditions of the simulation model (such as acoustic impedance matching at high temperature) are often based on theoretical assumptions and deviate from the actual test environment, the directly output simulation data usually cannot completely match the test data.

[0082] Therefore, this embodiment introduces a correction mechanism. Specifically, the system calculates the deviation between the experimental and simulated values. When the deviation exceeds a preset threshold (e.g., a sound velocity deviation greater than 2%), a model correction procedure is triggered. The correction procedure employs a reverse adjustment strategy. For example, if the experimentally measured sound velocity is lower than the simulated value, the system will adjust the elastic constant matrix or grain boundary scattering coefficient of the material in the simulation model in reverse, iteratively calculating until the deviation between the simulation output and the experimental data converges to an acceptable range. This correction process based on experimental data essentially "calibrates" the simulation model using real-world operating data, enabling it to predict unknown operating conditions. The correction targets are not limited to material parameters but can also include boundary conditions, such as adjusting the acoustic impedance of the model surface to simulate coupling effects under different medium flow states.

[0083] After model correction, the system generates a mapping dataset between temperature, medium, and sound field parameters based on the corrected simulation model. Since the corrected model has been experimentally verified, its calculation results have higher reliability. Therefore, it can be used to extrapolate and calculate sound field parameters over a wider temperature range or under different medium flow rates, thereby generating a mapping dataset covering all operating conditions.

[0084] Furthermore, a mapping dataset between temperature, medium, and sound field parameters is generated, including: fitting the mapping dataset to obtain a mapping function using regression analysis or a neural network algorithm; or, constructing a multidimensional mapping database that stores the correspondence between temperature, medium, and sound field parameters.

[0085] To facilitate engineering applications, this embodiment provides two specific output formats for the mapping relationship: First form: Mapping function The system can employ regression analysis methods, such as polynomial fitting, to fit a discrete mapping dataset and obtain a continuous functional expression for the sound velocity or attenuation coefficient with respect to temperature and medium flow velocity, such as... .

[0086] This function allows the online detection system to calculate the theoretical sound field parameters under the current operating conditions in real time, eliminating the need for complex simulation calculations and greatly improving detection efficiency. Furthermore, for mapping relationships with significant nonlinear characteristics, neural network algorithms (such as BP neural networks) can be used for training, with temperature and medium parameters as the input layer and sound field parameters as the output layer, to obtain a high-precision mapping model.

[0087] The second form: Multidimensional mapping database The system stores parameters such as temperature, medium type, medium flow rate, sound velocity, and attenuation coefficient in a database in the form of lookup tables. During actual testing, the system quickly retrieves and interpolates the corresponding sound field reference parameters from the database based on the real-time acquired temperature and medium data. This approach is suitable for scenarios with extremely high real-time requirements and discrete operating conditions.

[0088] This embodiment transforms complex physical simulation and experimental data into a data model that is easy to use in engineering applications through the two output formats described above, providing direct criterion support for the online inspection of high-temperature metal pipelines. In terms of performance, the simulation model corrected based on experimental data can reduce its sound velocity prediction deviation to within 2%, significantly improving accuracy compared to the uncorrected initial simulation model. Simultaneously, the mapping function format allows the online inspection system to obtain theoretical sound field parameters under current operating conditions in real time without complex simulation calculations, greatly improving the inspection response speed. The multi-dimensional mapping database format supports rapid retrieval and interpolation in discrete operating scenarios. Both formats together ensure the real-time performance and accuracy of the mapping relationship in engineering applications.

[0089] This embodiment establishes a simulation-experiment closed-loop correction mechanism (such as...). Figure 4 (As shown). Specific steps: Test data were obtained at nine operating points, including three temperature points (400℃, 500℃, and 600℃) and three flow velocity points (10m / s, 20m / s, and 30m / s). The initial simulation model output is compared with the experimental data, and the deviation matrix is ​​calculated. A reverse adjustment strategy is adopted: if the simulated sound velocity is higher than the measured value, the stiffness coefficient in the elastic constant matrix is ​​reduced and the simulation is repeated. Iterate until the average deviation of 9 operating points is <2%; The extrapolated data of the corrected model were fitted using second-order polynomial regression to obtain the mapping function.

[0090] The corrected model showed an average sound velocity deviation of 1.2% and a maximum deviation of 1.8% at the 9 calibration points. At the uncalibrated verification point at 550℃ / 15m / s, the predicted sound velocity deviated from the measured value by 1.5%, verifying the extrapolation accuracy. The mapping function form is as follows:

[0091] Where v is in m / s, Unit: ℃ Unit: m / s), goodness of fit R 2 =0.994; the online detection system takes 0.8ms to call the mapping function to calculate the theoretical speed of sound, which is 3,375,000 times more efficient than rerunning the simulation model (45 minutes).

[0092] Example 5 This embodiment uses a long-range sootblower tube of a coal-fired boiler as an example to explain in detail the specific application scenarios of the above-mentioned high-temperature sound field mapping construction method for sootblower tubes. Currently, sootblower tubes operate under harsh conditions with temperatures exceeding 400℃ and accompanied by steam scouring. Traditional shutdown inspection methods have significant blind spots in safety monitoring, and manually inspecting each point of a tube over ten meters long is extremely inefficient and risky. Therefore, there is an urgent need for a sound field mapping foundation that can support online inspection. Although this embodiment uses a sootblower tube as an example, the method of this invention is also applicable to other high-temperature pressure pipelines, such as main steam pipelines and reheat steam pipelines.

[0093] Specifically, the high-temperature metal pipe is a sootblower barrel, configured to operate in a high-temperature environment above 400°C and withstand the scouring of steam media. In the application scenario of this embodiment, the sootblower barrel is approximately 16 meters long, with an outer diameter of approximately 140 millimeters, and is typically made of heat-resistant alloy steel. This barrel is continuously exposed to the high-temperature environment of the furnace, enduring not only unsteady high-temperature oxidation but also the continuous scouring of high-velocity steam media, as well as complex mechanical stresses and alternating thermal stresses generated during rotation and expansion. This harsh operating environment makes the barrel highly susceptible to problems such as blown-off thinning and weld thermal fatigue fracture, urgently requiring online detection and monitoring.

[0094] When applying the method provided by this invention, a simulation model incorporating microstructural characteristics is first constructed for the sootblower barrel of this specific specification and material. Since the barrel material undergoes microstructural aging (such as carbide precipitation and grain coarsening) during long-term operation at high temperatures, leading to changes in sound wave propagation characteristics, a grain size distribution parameter reflecting the material's aging state is specifically introduced into the simulation model. Secondly, a high-temperature test platform is built, and short-tube samples of the same material and process as the actual barrel are taken. At multiple temperature gradients such as 400℃, 500℃, and 600℃, the condition of steam medium scouring the pipe surface at different flow rates is simulated, and real acoustic response data are collected. Finally, based on the comparison and correction of simulation and experimental data, a database of mapping relationships between temperature, steam velocity, sound velocity, and attenuation coefficient for the sootblower barrel material is generated.

[0095] This embodiment applies the above method to the sootblower barrel, achieving precise control over the sound field propagation pattern under high-temperature operation. Traditional detection methods require shutdown, resulting in significant blind spots in safety monitoring, and manual inspection of a barrel over ten meters long is extremely inefficient. The mapping relationship constructed in this embodiment provides accurate reference parameters for the online ultrasonic testing system, making it possible to monitor defects such as barrel wall thinning and internal cracks in real time without shutdown or disassembly. In terms of effectiveness, this embodiment, for a sootblower barrel approximately 16 meters long and 140 mm in outer diameter, constructed a mapping database of temperature, steam velocity, sound velocity, and attenuation coefficient at multiple temperature gradients (400℃, 500℃, 600℃). Compared to traditional shutdown-based testing methods, this eliminates safety monitoring blind spots, shortens the detection response time from several hours after shutdown to real-time acquisition during operation, significantly reduces maintenance risks, and improves the operational reliability of the sootblowing system.

[0096] For 12 long-range sootblower barrels (material 12Cr1MoV, length 16m, outer diameter 140mm, wall thickness 8mm) of a 630MW coal-fired boiler in a power plant, this invention's method was used to construct a database of temperature-steam velocity-sound velocity-attenuation coefficient mapping relationships for the barrel material. Based on this database, an online ultrasonic testing system was developed, with four sets of ultrasonic transducers (spaced 4m apart along the length) installed on the outside of the barrels to collect echo signals in real time. The system automatically compensates for the effects of temperature and flow velocity by calling the mapping relationships and calculates the wall thickness value.

[0097] Monitoring data from three consecutive months of operation shows that: Detection response time: Traditional downtime detection requires 4.5 hours per piece, and 12 pieces require 54 hours; the online detection of this invention enables real-time monitoring with a response time of <1 second; Defect detection rate: Traditional methods found 2 severe thinning (wall thickness reduction >30%) within 3 months, while the method of this invention found 7 early thinning (thinning 15%~20%) and 3 microcracks (length <5mm), of which 5 were confirmed by shutdown verification; Effective early warning lead time: On average, developing defects are detected 23 days in advance, providing ample preparation time for planned shutdowns and maintenance; Economic benefits: Avoids one unplanned shutdown (loss of approximately RMB 3 million per day), extends the barrel replacement cycle from 18 months to 28 months, and saves approximately RMB 850,000 in annual operation and maintenance costs per unit.

[0098] Example 6 In another embodiment of the present invention, a high-temperature sound field mapping construction system for sootblower barrels is provided. This system can be used to implement the above-mentioned high-temperature sound field mapping construction method for sootblower barrels. Specifically, the high-temperature sound field mapping construction system for sootblower barrels includes a model construction module, an experimental data acquisition module, and a mapping relationship generation module.

[0099] The model building module is configured to construct a simulation model containing microstructural features, which is used to simulate the propagation behavior of sound waves in the target material. In terms of hardware implementation, the model building module typically includes a high-performance processor (such as a CPU or GPU) and memory. The processor is configured to run finite element analysis software or a self-developed acoustic field simulation program. By calling pre-stored parameters such as the material grain size distribution function and elastic constant matrix in memory, it executes the Voronoi algorithm to generate the grain topology and solve the wave equation. The model building module is not limited to a specific computing unit; any hardware platform with numerical computation capabilities, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), can be used to implement the functionality of this module.

[0100] The experimental data acquisition module is configured to acquire acoustic response data under high-temperature testing conditions. This module serves as the interface between the device and the physical testing environment. At the hardware level, the experimental data acquisition module includes a data acquisition card, a signal conditioning circuit, and a communication interface. The data acquisition card connects to the ultrasonic transducer in the high-temperature testing platform via a cable to acquire the raw sound field signal. The signal conditioning circuit performs amplification, filtering, and other preprocessing on the acquired weak signal. The communication interface receives operating data from sensors such as temperature sensors and flow meters, ensuring that the acoustic response data is recorded synchronously with temperature and medium parameters. Through this hardware configuration, the experimental data acquisition module can acquire acoustic signals in real time and accurately under high-temperature conditions.

[0101] The mapping relationship generation module is configured to generate mapping relationships between temperature, medium, and sound field parameters based on the comparison results between the output of the simulation model and the acoustic response data. This module is the core of the device's data processing. In terms of hardware implementation, the mapping relationship generation module relies on a data processing program running on the processor. This program is configured to execute a comparison algorithm, calculate the deviation between the simulation data and the experimental data, and correct the parameters of the simulation model in reverse based on the deviation. After correction, the processor further executes regression analysis or neural network training algorithms to generate mapping functions or construct a multi-dimensional mapping database, and stores the results in memory. The mapping relationship generation module and the model building module can physically share the same processor, with functional division achieved through different software logic units.

[0102] Example 7 This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used in the operation of a method for constructing a high-temperature sound field mapping of a sootblower barrel, including: A simulation model incorporating microstructural features is constructed, configured to simulate the propagation behavior of sound waves in a target material; acoustic response data is acquired under a high-temperature test environment; based on the comparison between the output of the simulation model and the acoustic response data, a mapping relationship between temperature, medium, and sound field parameters is generated.

[0103] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the high-temperature sound field mapping construction method for the sootblower barrel in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the high-temperature sound field mapping construction system for the sootblower barrel in this embodiment. To avoid repetition, these details are not elaborated here.

[0104] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0105] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0106] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0107] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0108] Please see Figure 6 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0109] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0110] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0111] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0112] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0113] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0114] Example 8 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0115] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0116] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0117] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the high-temperature sound field mapping construction method for sootblower barrels in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: A simulation model incorporating microstructural features is constructed, configured to simulate the propagation behavior of sound waves in a target material; acoustic response data is acquired under a high-temperature test environment; based on the comparison between the output of the simulation model and the acoustic response data, a mapping relationship between temperature, medium, and sound field parameters is generated.

[0118] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0119] based on Figure 3 The microstructure simulation model shown is based on the construction principle of this invention. This invention conducts a systematic simulation study on the grain structure of 12Cr1MoV heat-resistant alloy steel under different aging states, and the results are as follows:

[0120] The simulation results reveal a pattern: for every 10 μm increase in grain size, the sound velocity decreases by approximately 25-30 m / s, while the attenuation coefficient increases by approximately 0.02-0.03 dB / mm. This quantitative relationship provides a theoretical basis for inferring the degree of material aging based on acoustic parameters.

[0121] based on Figure 4 The simulation-experiment closed-loop correction process shown in this invention generates the following temperature-velocity of sound mapping data:

[0122] Iterative convergence process analysis: The average deviation was 4.8% after the first correction, 2.3% after the second correction, and 1.2% after the third correction, verifying the effectiveness of the closed-loop correction mechanism. After three corrections, the maximum prediction deviation of the model in the 400-600℃ range did not exceed 1.5%, fully meeting the requirements for engineering applications.

[0123] In summary, this invention, a method and system for constructing high-temperature sound field mapping for sootblower barrels, systematically solves the core problem of accurately obtaining sound field propagation laws under high-temperature environments through a complete technical path including microstructure simulation modeling, high-temperature test data acquisition, simulation and test closed-loop correction, and mapping relationship output. First, by introducing a microstructure simulation model of grain size distribution and grain boundary orientation difference distribution, the accuracy of sound velocity prediction under high-temperature conditions is improved from a deviation of >10% in traditional models to <2%, and the accuracy of attenuation coefficient prediction is improved from a deviation of >60% to <5%. Second, through a high-temperature test platform and wavelet threshold noise reduction processing, it achieves high-temperature sound field mapping for indoor environments. The system achieves precise temperature control up to 800℃ (±2℃), and improves the repeatability of sound velocity measurement to ±0.5%. Third, through a simulation-experiment closed-loop correction mechanism, the calibrated model can be extrapolated to cover the entire temperature and medium conditions, extending the operating range from finite discrete points to continuous space. Fourth, when applied to a 16-meter-long, 140-mm-diameter sootblower barrel, it enables real-time monitoring during operation, reducing the detection response time from several hours after shutdown to less than one second, significantly improving defect detection rate and early warning. This provides a solid theoretical foundation and data support for high-temperature online detection of sootblower barrels, demonstrating promising engineering application prospects and economic benefits.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0126] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0127] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for constructing a high-temperature sound field mapping for a sootblower barrel, characterized in that, Includes the following steps: S1. Construct a simulation model that includes microstructure characteristics, wherein the simulation model is configured to simulate the propagation behavior of sound waves in the target material; S2. Acquire acoustic response data under high-temperature test conditions; S3. Based on the comparison between the output of the simulation model and the acoustic response data, generate the mapping relationship between temperature, medium and sound field parameters.

2. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 1, characterized in that, The construction of the simulation model including microscopic organizational features includes: A geometric model is constructed based on the grain size distribution function and elastic constant matrix of the material; multiphysics coupling boundary conditions are set for the geometric model.

3. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 2, characterized in that, The geometric model is constructed based on the material's grain size distribution function and elastic constant matrix, including: The grain geometry topology is generated using the Voronoi polygon algorithm; based on the grain geometry topology, the wave equation is solved using the finite element method to obtain the sound field distribution data.

4. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 1, characterized in that, The acquisition of acoustic response data under high-temperature test conditions includes: A high-temperature testing platform is provided, which includes a heating chamber, a sample fixing unit, an ultrasonic transducer, and a data acquisition module. Under different temperature conditions and medium flow parameters, the ultrasonic transducer collects sound field signals, and the data acquisition module obtains the acoustic response data.

5. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 4, characterized in that, After acquiring the acoustic response data through the data acquisition module, the process further includes: The sound field signal is subjected to noise reduction processing; acoustic feature parameters are extracted from the noise-reduced sound field signal, the acoustic feature parameters including sound velocity and attenuation coefficient.

6. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 1, characterized in that, The comparison between the output of the simulation model and the acoustic response data generates a mapping relationship between temperature, medium, and sound field parameters, including: Based on the acoustic response data, the material parameters or boundary conditions of the simulation model are corrected; based on the corrected simulation model, a mapping dataset between temperature, medium and sound field parameters is generated.

7. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 6, characterized in that, The generated dataset mapping the temperature, medium, and sound field parameters includes: The mapping dataset is fitted using regression analysis or neural network algorithms to obtain a mapping function; or a multidimensional mapping database is constructed, which stores the correspondence between temperature, medium, and sound field parameters.

8. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 1, characterized in that, The high-temperature metal pipe is a sootblower barrel, which is configured to operate in a high-temperature environment above 400°C and withstand the scouring of steam medium.

9. The method for constructing high-temperature sound field mapping of a sootblower barrel according to claim 1, characterized in that, The microstructure features include grain size distribution and grain boundary orientation difference distribution.

10. A high-temperature sound field mapping and construction system for a sootblower barrel, characterized in that, include: The model building module is configured to build a simulation model containing microstructure features, which is used to simulate the propagation behavior of sound waves in the target material; The test data acquisition module is configured to acquire acoustic response data under high-temperature test conditions. The mapping relationship generation module is configured to generate a mapping relationship between temperature, medium, and sound field parameters based on the comparison results between the output of the simulation model and the acoustic response data.

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