Thermal resistance network thermocouple rake measurement temperature correction method

Abstract

The application relates to the technical field of high-temperature gas flow field measurement, in particular to a thermal resistance network thermocouple rake measurement temperature correction method, wherein the method comprises the following steps: establishing a computational fluid dynamics model, performing numerical simulation under a plurality of preset incoming flow conditions, and obtaining initial data of the computational fluid dynamics; obtaining a corrected numerical method and a turbulent flow model according to a first error correction model of calibration test measurement data and initial data; constructing a thermal resistance relationship network and a thermal resistance network model; adjusting nodes and modules in the thermal resistance relationship network according to a second error between simulation data and output data of the thermal resistance network model, obtaining a thermal resistance relationship network matched with the simulation data and a correction coefficient of a heat transfer process correlation formula; and calculating a measurement temperature correction value of the thermocouple rake by using the thermal resistance network model. Therefore, the problems that related technologies only rely on a computational fluid dynamics correction scheme with high time cost, only rely on an empirical correlation formula with low correction precision, only rely on test calibration with high economic cost and the like are solved.

Description

Technical Field

[0001] This application relates to the field of high-temperature gas flow field measurement technology, and in particular to a method for temperature correction in thermocouple rake measurement using a thermal resistance network. Background Technology

[0002] Accurate measurement of airflow temperature in high-temperature environments such as the combustion chamber and tail nozzle of an aero-engine is of great significance for evaluating engine performance and ensuring the safety of hot-end components. Thermocouples are usually used to directly measure the temperature, but the measurement results are easily affected by radiation and thermal conduction errors, deviating from the actual gas temperature. Without correction, the error can be more than 100 degrees Celsius.

[0003] To reduce the impact of heat transfer errors on thermocouple measurement accuracy, experimental data can be used to verify numerical methods and turbulence models in computational fluid dynamics, thereby obtaining accurate velocity and temperature field distributions and ultimately correcting the thermocouple rake temperature measurement error. However, this method requires substantial computational resources and suffers from poor real-time performance. While simplified calculation methods based on a holistic model offer high computational efficiency, their reliance on empirical correlations makes it difficult to accurately characterize flow and heat transfer characteristics under complex geometries, resulting in insufficient measurement accuracy. Methods based on full-condition experimental measurement error calibration offer the highest accuracy, but their calibration cost far exceeds the manufacturing cost of the thermocouple rake. Therefore, there is an urgent need to develop a thermocouple rake temperature measurement error correction method that balances high accuracy, short calibration time, and high efficiency. Summary of the Invention

[0004] This application provides a method for correcting the temperature measured by a thermocouple rake using a thermal resistance network, in order to solve the problems of high experimental costs, long numerical calculation time, and inaccurate empirical correlations in the three existing schemes.

[0005] The first aspect of this application provides a method for correcting the temperature measurement of a thermocouple rake using a thermal resistance network, comprising the following steps: establishing a computational fluid dynamics (CFD) model, performing numerical simulations under multiple preset incoming flow conditions to obtain initial CFD data; correcting the CFD model based on a first error to obtain a numerical method and turbulence model that match the calibration test measurement data; performing numerical simulations under preset incoming flow conditions based on the numerical method and turbulence model to obtain CFD simulation data, wherein the first error is the numerical difference between the calibration test measurement data and the initial CFD data under the preset incoming flow conditions; constructing a thermal resistance network, wherein the thermal resistance network includes multiple temperature nodes and thermal resistance modules, and constructing a thermal resistance network model based on the convective heat transfer coefficient correlation, the radiation intensity correlation, the flow coefficient, and the thermal resistance network; adjusting the temperature nodes and thermal resistance modules in the thermal resistance network based on a second error to obtain a thermal resistance network and heat transfer process correlation correction coefficient that match the CFD simulation data, wherein the second error is the numerical difference between the CFD simulation data and the output data of the thermal resistance network model under the preset incoming flow conditions; and calculating the corrected temperature value of the thermocouple rake using the thermal resistance network model.

[0006] Optionally, in one embodiment of this application, obtaining initial computational fluid dynamics (CFD) data includes: establishing a CFD model based on the geometric structure data of the thermocouple sensing part, thermocouple wire, heat shield, and mounting base in the thermocouple rake; performing CFD numerical simulations on the flow field and temperature of the thermocouple rake under multiple preset inflow conditions to obtain velocity field distribution data and temperature field distribution data; and calculating the convective heat transfer coefficient correlation, radiation intensity correlation, and flow coefficient based on the velocity field distribution data and temperature field distribution data.

[0007] Optionally, in one embodiment of this application, the temperature node includes the sensing part of the thermocouple and other nodes, the other nodes including at least one of the measured hot gas, thermocouple wire, heat shield, rake body and environment.

[0008] Optionally, in one embodiment of this application, constructing a thermal resistance network includes: dividing the component structure of the thermocouple rake into multiple temperature nodes; obtaining the heat transfer relationship between the multiple temperature nodes; establishing a thermal resistance module based on the heat transfer relationship between the multiple temperature nodes; and generating a thermal resistance network based on the multiple temperature nodes and the thermal resistance module, wherein the thermal resistance module includes at least one of a convective thermal resistance, a conductive thermal resistance, and a radiative thermal resistance group.

[0009] Optionally, in one embodiment of this application, the component structure of the thermocouple rake is divided into multiple temperature nodes, including: obtaining the heat transfer path and temperature distribution characteristics of each component in the component structure; performing segmented discretization processing on the component structure according to the heat transfer path and temperature distribution characteristics; and determining multiple temperature nodes according to the results of the segmented discretization processing.

[0010] Optionally, in one embodiment of this application, generating a thermal resistance relationship network based on multiple temperature nodes and thermal resistance modules includes: determining the corresponding thermal resistance type based on the heat transfer relationship between multiple temperature nodes; performing equivalent characterization on the heat transfer path between multiple temperature nodes based on the thermal resistance type; determining the thermal resistance based on the result of the equivalent characterization, and determining the connection method between the temperature nodes and the thermal resistance; and generating a thermal resistance relationship network based on multiple temperature nodes, thermal resistance, and connection method.

[0011] Optionally, in one embodiment of this application, the preset inflow conditions include different combinations of inflow velocity, inflow temperature and inflow pressure.

[0012] Optionally, in one embodiment of this application, calculating the measurement temperature correction value of the thermocouple rake using a thermal resistance network model includes: obtaining arbitrary incoming flow conditions for the thermocouple rake; inputting the arbitrary incoming flow conditions into the thermal resistance network model, and outputting the temperature values ​​and flow velocity parameters of each temperature node through the thermal resistance network model; extracting the temperature value of the sensing part of the thermocouple in the temperature node, and using the temperature value of the sensing part of the thermocouple as the measurement temperature correction value of the thermocouple rake.

[0013] Optionally, in one embodiment of this application, the temperature values ​​and flow velocity parameters of each temperature node are output through a thermal resistance network model, including: calculating the actual flow velocity under any incoming flow conditions based on the flow coefficient; calculating the convective heat transfer coefficient under any incoming flow conditions based on the correlation formula of the convective heat transfer coefficient; and calculating the total temperature and static temperature of each temperature node based on the actual flow velocity, flow coefficient, convective heat transfer coefficient and thermal resistance network.

[0014] Therefore, this application has the following beneficial effects: First, a computational fluid dynamics (CFD) model is established, and numerical simulations are performed under multiple preset inflow conditions to obtain initial CFD data. Based on the first error between the calibration test measurement data and the initial CFD data under the preset inflow conditions, the CFD model is corrected to obtain a numerical method and turbulence model that matches the calibration test measurement data, thereby obtaining CFD simulation data. Second, a thermal resistance network including multiple temperature nodes and thermal resistance modules is constructed, and a thermal resistance network model is built based on the correlations of convective heat transfer coefficients, radiation intensity, flow coefficients, and thermal resistance relationships. Then, based on the second error between the CFD simulation data and the output data of the thermal resistance network model under the preset inflow conditions, the temperature nodes and thermal resistance modules in the thermal resistance network are adjusted to obtain a thermal resistance network and heat transfer process correlation correction coefficients that match the CFD simulation data. Finally, the measured temperature correction value of the thermocouple rake is calculated using the thermal resistance network model. In summary, this application, based on the principle of similarity, extracts corresponding dimensionless quantities, which can significantly reduce the number of operating conditions in experiments and numerical simulations. This reduces the experimental costs and numerical simulation time costs required for training the thermal resistance network model. Simultaneously, it balances the advantages of experiments, simulations, and thermal resistance networks, achieving the beneficial effects of low cost in calibrating numerical models, short training time in numerical simulations, and high accuracy in thermal resistance network calculations. Therefore, it solves the problems of related technologies that combine heat shields and computational fluid dynamics to obtain correction parameters, resulting in high computational costs, reliance on empirical correlations, and insufficient measurement accuracy.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a temperature correction method for thermocouple rake measurement using a thermal resistance network according to an embodiment of this application; Figure 2 This is a front view of the thermocouple rake temperature measuring assembly structure according to an embodiment of this application; Figure 3 This is a left view of the thermocouple rake temperature measuring assembly structure according to an embodiment of this application; Figure 4 This is a top view of the thermocouple rake temperature measuring assembly structure according to an embodiment of this application; Figure 5 This is an isometric view of the thermocouple rake temperature measuring assembly structure according to an embodiment of this application; Figure 6 This is a schematic diagram of heat transfer relationships according to an embodiment of this application; Figure 7 This is a schematic diagram of the thermal resistance network topology according to an embodiment of this application. Detailed Implementation

[0017] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0018] The following describes a method for correcting the temperature measurement of a thermocouple rake using a thermal resistance network, based on embodiments of the present application, with reference to the accompanying drawings. Addressing the problems mentioned in the background section, the present application provides a method for correcting the temperature measurement of a thermocouple rake using a thermal resistance network. In this method, firstly, a computational fluid dynamics (CFD) model is established, and numerical simulations are performed under multiple preset inflow conditions to obtain initial CFD data. Then, the CFD model is corrected based on a first error between the calibration test measurement data and the initial CFD data under the preset inflow conditions, to obtain a numerical method and turbulence model that match the calibration test measurement data, thereby obtaining CFD simulation data. Secondly, a thermal resistance network including multiple temperature nodes and thermal resistance modules is constructed, and a thermal resistance network model is built based on the convective heat transfer coefficient correlation, radiation intensity correlation, flow coefficient, and thermal resistance network. Then, based on a second error between the CFD simulation data and the output data of the thermal resistance network model under the preset inflow conditions, the temperature nodes and thermal resistance modules in the thermal resistance network are adjusted to obtain a thermal resistance network and heat transfer process correlation correction coefficient that match the CFD simulation data. Finally, the corrected temperature measurement value of the thermocouple rake is calculated using the thermal resistance network model. In summary, this application, based on the principle of similarity, extracts corresponding dimensionless quantities, which can significantly reduce the number of operating conditions in experiments and numerical simulations. This reduces the experimental costs and numerical simulation time costs required for training the thermal resistance network model. Simultaneously, it balances the advantages of experiments, simulations, and thermal resistance networks, achieving the beneficial effects of low cost in calibrating numerical models, short training time in numerical simulations, and high accuracy in thermal resistance network calculations. Therefore, it solves the problems of related technologies that combine heat shields and computational fluid dynamics to obtain correction parameters, resulting in high computational costs, reliance on empirical correlations, and insufficient measurement accuracy.

[0019] Specifically, Figure 1 This is a flowchart illustrating a method for correcting temperature measurement using a thermocouple rake with a thermal resistance network, as provided in an embodiment of this application.

[0020] like Figure 1 As shown, the temperature correction method for thermocouple rake measurements using a thermal resistance network includes the following steps: In step S101, a computational fluid dynamics model is established, and numerical simulations are performed under multiple preset inflow conditions to obtain initial computational fluid dynamics data.

[0021] The computational fluid dynamics model is the physical and mathematical calculation model of fluid flow and heat transfer used in numerical simulation; the incoming flow conditions are the fluid state parameters entering the thermocouple measurement area. In this application, the preset incoming flow conditions include different combinations of incoming flow velocity, incoming flow temperature and incoming flow pressure; the initial computational fluid dynamics data in this application represent the physical quantity data calculated by numerical simulation using the unmodified computational fluid dynamics model.

[0022] Understandably, establishing a computational fluid dynamics model and conducting numerical simulations under multiple preset inflow conditions to obtain initial computational fluid dynamics data can efficiently obtain the flow field, temperature field, and heat transfer parameters around the thermocouple rake under different operating conditions, providing accurate and reliable basic inputs for subsequent thermal resistance network modeling and temperature correction.

[0023] In one embodiment of this application, obtaining initial computational fluid dynamics (CFD) data includes: establishing a CFD model based on the geometric structure data of the thermocouple sensing part, thermocouple wire, heat shield, and mounting base in the thermocouple rake; performing CFD numerical simulations on the flow field and temperature of the thermocouple rake under multiple preset inflow conditions to obtain velocity field distribution data and temperature field distribution data; and calculating the convective heat transfer coefficient correlation, radiation intensity correlation, and flow coefficient based on the velocity field distribution data and temperature field distribution data.

[0024] Here, the flow field is a physical field used to characterize the flow state of fluid around the thermocouple rake; the flow field distribution data is a set of information describing the spatial distribution of fluid velocity, pressure, and flow state within the computational domain, representing, in this embodiment, the velocity field, pressure field, and flow structure characteristic data of the airflow inside the combustion chamber obtained through numerical simulation; the temperature field distribution data describes the thermal state information of temperature variation with spatial location within the computational domain, representing, in this embodiment, the temperature distribution of gas and related structural surfaces inside the combustion chamber obtained through numerical simulation; numerical simulation describes the actual flow and heat transfer process using equations (such as mass conservation, momentum conservation, and energy conservation equations), and then solves them numerically using a computer to simulate the changes in the flow field and temperature field under different operating conditions, typically including calculations... The implementation methods include computational fluid dynamics numerical simulation; the convective heat transfer coefficient correlation is a mathematical expression used to characterize the functional relationship between the convective heat transfer coefficient and the incoming flow conditions. In this application, it represents an empirical model reflecting the change law of the heat transfer capacity of the thermocouple probe surface with the incoming flow velocity, temperature, and pressure; the radiation intensity correlation is a mathematical expression used to characterize the energy exchange relationship between different objects or surfaces through thermal radiation. This expression is based on parameters such as temperature, surface radiation characteristics, and geometric relationships, and is used to describe the functional relationship between the radiative heat transfer or equivalent radiative heat transfer intensity and the relevant physical quantities; the flow coefficient is a characterizing parameter of the difference between the actual flow state and the ideal flow state. In this application, it represents the correction coefficient for the degree of flow velocity attenuation near the probe caused by the flow resistance introduced by the thermocouple rake and the heat shield.

[0025] It is understood that the embodiments of this application obtain flow field distribution data and temperature field distribution data by performing numerical simulation on thermocouple rakes under multiple preset inflow conditions, thereby calculating convective heat transfer coefficient, radiation intensity correlation and flow coefficient that match the actual working conditions. This provides accurate heat transfer parameters and boundary condition inputs for the subsequent construction of thermal resistance network model, enabling the thermal resistance network model to truly reflect the heat transfer process in the measurement system and transforming the complex flow-heat transfer coupling problem into a computable parameterized model, thereby improving the accuracy of temperature correction results.

[0026] In this embodiment of the application, the thermocouple rake temperature measuring component structure arranged inside the thermocouple rake is as follows: Figures 2 to 5 As shown, where, Figure 2 Main view, Figure 3 For left view, Figure 4 Top view, Figure 5 This is an axonometric drawing.

[0027] The front view shows the front structure of the thermocouple rake body, demonstrating the distribution of heat flow outlet holes and the arrangement of internal thermocouples; the left view shows the lateral outline of the thermocouple rake and the cross-sectional position of the thermocouples within the rake body; the top view shows the top structural layout, indicating the spatial position of the thermocouple sensing part and the thermocouple wires; and the isometric view shows the overall configuration of the thermocouple rake in three dimensions, reflecting the spatial relationship and assembly features between the rake body and the heat shield.

[0028] The above multi-perspective structural diagrams can comprehensively characterize the geometric configuration and key component distribution of the thermocouple rake, providing a reliable structural basis for subsequent fluid dynamics modeling, heat transfer path identification, and thermal resistance network model construction.

[0029] In this embodiment, a computational fluid dynamics model is established based on component structure data. Numerical simulations are performed on the thermocouple rake under multiple preset inflow conditions to obtain the flow state and temperature distribution of the fluid around it. The flow field distribution data reflects the velocity, pressure, and turbulence characteristics of the hot gas around the thermocouple sensing part and the heat shield, while the temperature field distribution data reflects the temperature distribution and heat transfer results between the fluid and the solid structure.

[0030] Based on the flow field distribution data, flow characteristic parameters of the hot gas at the inlet and inside of the heat shield can be obtained, such as the velocity distribution and the flow rate variation of the fluid through the opening region. This allows for the calculation of the flow coefficient, which characterizes the ability of the hot gas to enter the heat shield. This flow coefficient describes the degree of deviation between the actual flow and the ideal flow and is an important parameter for the flow boundary conditions in the subsequent thermal resistance network model.

[0031] Simultaneously, by combining flow field distribution data and temperature field distribution data, the heat transfer process between the thermocouple sensing element and the inner and outer walls of the heat shield and the surrounding hot combustion gas can be determined. Specifically, by analyzing the temperature gradient and flow state of the fluid near the wall, the convective heat transfer coefficients between the thermocouple sensing element and the hot combustion gas, as well as between the inner and outer walls of the heat shield and the hot combustion gas, can be calculated. This convective heat transfer coefficient is used to characterize the intensity of convective heat transfer between the fluid and the solid surface and is an important basis for calculating convective thermal resistance in the thermal resistance network model.

[0032] Furthermore, based on temperature field distribution data and the spatial relationship of component structures, the radiation intensity correlations between different surfaces can be determined. Specifically, these include the radiation intensity correlations between the thermocouple rake sensing part and the inner wall of the heat shield, the radiation intensity correlations between the outer wall of the heat shield and the ambient radiation intensity, and the flow rate coefficient of the hot gas flowing into the thermocouple rake heat shield. Among these, the radiation intensity correlations are used to describe the energy exchange between the surfaces through thermal radiation and are equivalent to radiation thermal resistance or related parameters in subsequent modeling.

[0033] Through the above process, the flow and heat transfer information obtained from numerical simulation is transformed into key parameters such as convective heat transfer coefficient, radiation intensity relationship formula and flow coefficient, thereby providing accurate input for the subsequent construction of thermal resistance network model, so that the established thermal resistance network model can effectively characterize the heat transfer characteristics of thermocouple rake under actual working conditions and improve the accuracy of temperature correction results.

[0034] In one embodiment of this application, the preset inflow conditions include different combinations of inflow velocity, inflow temperature and inflow pressure.

[0035] It is understood that the embodiments of this application discretize the future flow conditions into multiple sets of representative parameter combinations, enabling calibration and calculation under different speed, temperature and pressure conditions, thus avoiding the problem of limited applicability caused by relying on only a single condition.

[0036] In step S102, based on the first error correction computational fluid dynamics model, a numerical method and turbulence model matching the calibration test measurement data are obtained. Based on the numerical method and turbulence model, numerical simulation is performed under preset incoming flow conditions to obtain computational fluid dynamics simulation data. The first error is the numerical difference between the calibration test measurement data and the initial computational fluid dynamics data under the preset incoming flow conditions.

[0037] Among them, calibration experimental measurement data refers to physical quantity data directly obtained through experimental testing or on-site measurement. In this application embodiment, it represents the actual temperature and related parameter data obtained by thermocouples or other measurement methods under corresponding preset inflow conditions; computational fluid dynamics simulation data refers to physical quantity data calculated by numerical simulation methods. In this application embodiment, it represents the temperature field and flow field corresponding parameter data calculated based on the modified fluid dynamics model; the first error is an evaluation index used to characterize the degree of deviation between simulation results and actual measurement results. In this application embodiment, it represents the difference between actual measurement data and simulation data or its functional form; the numerical method in this application includes at least one of mesh generation, boundary condition setting, and numerical solution parameters adjustment; the turbulence model is a mathematical model describing the characteristics of fluid turbulence. In this application, it represents the model type used to characterize fluid turbulence behavior; the modified turbulence model is the process of optimizing the turbulence model. In this application, it represents improving simulation accuracy by adjusting turbulence model parameters or changing the turbulence model type.

[0038] It is understood that the embodiments of this application introduce an error evaluation mechanism between calibration experimental measurement data and numerical simulation data to perform accuracy calibration of the computational fluid dynamics model before calculating the convective heat transfer coefficient, the radiative heat transfer relationship, and the flow coefficient.

[0039] Specifically, by calculating the first error between the calibrated experimental measurement data and the simulation data, and adjusting the calculation parameters and turbulence model of the computational fluid dynamics model when the first error exceeds a preset first threshold, the simulation results gradually approximate the experimental measurement results, thereby ensuring the reliability of the numerical simulation results. The preset first threshold is a preset standard used to determine whether the error meets the accuracy requirements; in this embodiment, it represents the upper limit of the allowable error set according to engineering accuracy requirements. By following the steps described above, the problem of error accumulation caused by directly using unverified numerical simulation results can be avoided. This allows the convective heat transfer coefficient, radiative heat transfer relationship, and flow coefficient calculated based on simulation data to more accurately reflect the flow and heat transfer characteristics under actual operating conditions. Furthermore, by iteratively correcting the model and introducing an error convergence criterion, adaptive improvement of model accuracy is achieved, enhancing the reliability of the parameter acquisition process while ensuring computational accuracy.

[0040] Finally, after error correction, the embodiments of this application use the following formulas to characterize the convective heat transfer coefficient, the radiation intensity correlation, and the flow coefficient (the following formulas are only applicable to the appendices of this application). Figure 6 (Thermocouple structure in the middle) Fourier's law of thermal conductivity applies to the calculation of thermal conductivity between the sensing part of a thermocouple and between the thermocouple wire and the probe body: (1) in, The heat conducted between the sensing part of the thermocouple, the thermocouple wire, and the body; It is thermal conductivity; It is the thermally conductive area (in specific calculations, it can be considered as the cross-sectional area of ​​the thermocouple wire); It is the temperature difference between the sensing part of the thermocouple and the root of the thermocouple wire (i.e., the connection point between the thermocouple and the rake body); This represents the length of the thermocouple wire.

[0041] The external spherical model is suitable for calculating convective heat transfer between the thermocouple sensing element and hot air. However, Equation 2 requires an infinitely large space, uniform flow, and a single sphere. In this calculation example, the thermocouple sensing element can be considered a sphere, but a thermocouple wire is connected to the back of the sphere. Furthermore, due to the space limitation of the heat shield, the thermocouple sensing element is not in an infinitely large space, and the flow is non-uniform, which does not meet the conditions for using the formula. Therefore, Equation 2 needs to be modified based on the computational fluid dynamics simulation results.

[0042] (2) in, The heat transfer coefficient between the sensing part of the thermocouple and the measured hot gas; It is the Reynolds number; It is a Prandtl number; It is the viscosity of air.

[0043] The outer flat plate model is suitable for calculating the convective heat transfer coefficient between the outer wall of the heat shield and the incoming hot gas flow. However, Equation 3 is only applicable to an infinitely large flat plate with uniform flow. In this calculation example, the heat shield is a cylinder. Equation 3 is commonly used in engineering to calculate the heat transfer coefficient of an outer cylindrical surface. To improve its calculation accuracy, Equation 3 needs to be modified based on the computational fluid dynamics simulation results.

[0044] (3) in, The heat transfer coefficient between the outer wall of the heat shield and the incoming hot gas flow; It is the length through which the gas flows.

[0045] The formulas for forced convection flow inside pipes are applicable to calculating the heat transfer coefficient inside heat shields. Formula 4 is used for forced convection turbulent flow inside the pipe, while Formula 5 can be used for laminar flow. The specific formula selection depends on the Reynolds number of the flow inside the pipe. Determine: When the Reynolds number >2300, choose formula 4; when the Reynolds number is... <2300, choose formula 5.

[0046] In the calculation examples of this application, the heat shield can be regarded as a pipe. However, due to the presence of obstacles (such as thermocouples and their wires) in the pipe, the internal flow state differs from the ideal pipe flow. Therefore, in order to improve the calculation accuracy, it is necessary to correct the coefficients of Equation 4 and Equation 5 based on the computational fluid dynamics simulation results.

[0047] (4) (5) in, The heat transfer coefficient between the inner wall of the heat shield and the measured hot gas; For the position of manager; This is the Darcy drag coefficient for turbulent flow inside the pipe.

[0048] A simplified radiation calculation model is applicable to radiative heat transfer between the heat shield and the sensing element, as well as radiative heat transfer between the heat shield and the environment. The formula is used when the surface area of ​​object 1 is negligible relative to object 2, and object 2 completely contains object 1 in space. In this example, the surface area of ​​object 1 is much smaller than that of object 2, but it cannot be considered completely negligible. To improve its calculation accuracy, the coefficients of formula 6 need to be corrected based on the computational fluid dynamics simulation results.

[0049] (6) Specifically, regarding radiative heat transfer between the heat shield and the sensing element, The heat transferred by radiation heat transfer. Emissivity of the thermocouple sensing element The area of ​​the thermocouple sensing part is... It is a constant (for example, it may be 5.67 in one embodiment of this application). It is the temperature of the thermocouple sensing element. It refers to the temperature of the heat shield; Regarding radiative heat transfer between the heat shield and the environment, The heat transferred by radiation heat transfer. For the emissivity of the outer surface of the heat shield, The outer surface area of ​​the heat shield. It is a constant (for example, it may be 5.67 in one embodiment of this application). It refers to the temperature of the heat shield. It refers to the ambient temperature.

[0050] In step S103, a thermal resistance network is constructed, which includes multiple temperature nodes and thermal resistance modules. A thermal resistance network model is constructed based on the correlation of convective heat transfer coefficient, radiation intensity correlation, flow coefficient and thermal resistance network.

[0051] In this application, the thermal resistance network is a network structure formed by connecting multiple temperature nodes and corresponding thermal resistance modules according to heat transfer relationships. In this embodiment, it represents a topological model used to describe the heat transfer path and energy distribution of the entire measurement system. Temperature nodes in this application represent the equivalent temperature points of key components or regions measured by thermocouple rakes. Thermal resistance modules are equivalent parameter units used to characterize the ease of heat transfer between nodes. In this application, they represent the actual heat transfer process as equivalent to a thermal resistance element with an impeding effect. The convective heat transfer coefficient correlation is a functional expression describing the relationship between the convective heat transfer coefficient and the incoming flow conditions. In this application, it represents a heat transfer coefficient calculation model based on numerical simulation and data fitting, with flow velocity, temperature, and pressure as variables. The flow coefficient is a correction parameter used to characterize the deviation of the actual flow state from the ideal flow state. In this application, it represents a quantification coefficient of the influence of flow resistance caused by thermocouple rakes and heat shields on local flow velocity. The thermal resistance network model is a calculation model constructed from temperature nodes and the thermal resistance network to describe the system's heat transfer behavior. In this application, it represents a discretized heat transfer solution model formed by connecting nodes with equivalent thermal resistance.

[0052] It is understood that, in this embodiment of the application, by introducing the calibrated convection heat transfer coefficient correlation and flow coefficient into the thermal resistance network, a unified thermal resistance network model is constructed, realizing the coupled calculation of multiple heat transfer mechanisms such as convection, conduction and radiation. This transforms the complex thermal flow field problem into a network problem that can be quickly solved through node energy balance, thereby significantly reducing computational complexity.

[0053] In one embodiment of this application, the temperature node includes the sensing part of a thermocouple and other nodes, which include at least one of the measured hot gas, thermocouple wire, heat shield, rake body and environment.

[0054] In this embodiment, the sensing part of the thermocouple is the measuring structure used to directly sense the airflow temperature and generate a thermoelectric potential signal. In this application embodiment, it represents the temperature measurement area where the front end of the thermocouple probe is in direct contact with the high-temperature incoming flow. Other nodes are auxiliary temperature nodes participating in the heat exchange process, excluding the thermocouple sensing part. In this application embodiment, they represent at least one of the measured hot gas node, thermocouple structure node, heat shield node, rake body node, and environmental node. The measured hot gas is a high-temperature gas medium flowing within the combustion chamber. In this application embodiment, it represents the incoming gas that performs convective heat exchange on the thermocouple. The thermocouple wire connects the sensing part and the thermocouple rake body. In this application embodiment, it represents the gas participating in the heat conduction and convective heat exchange process. The thermocouple rake is part of the overall temperature measurement structure of the process; the thermocouple rake body is a supporting component for fixing the thermocouple rake and its auxiliary structures, and in this application, it represents the installation structure that connects and positions the thermocouple rake and the thermocouple rake structure; the heat shield is a protective structure set outside the thermocouple to weaken the influence of radiative heat transfer, and in this application embodiment, it represents a shielding component that plays a role in heat insulation and flow field modulation of the thermocouple; the rake body is a structural component for supporting and fixing the thermocouple and the heat shield, and in this application embodiment, it represents a supporting structure that participates in heat conduction and affects the overall temperature distribution; the environment is the external low-temperature area other than the high-temperature airflow in the combustion chamber, and in this application embodiment, it represents the cold wall or external space that undergoes radiative heat transfer with the measurement embodiment of this application.

[0055] It is understandable that by discretizing the temperatures of the various components of the complex thermocouple rake and the surrounding thermal environment, the continuously distributed temperature field is transformed into a finite number of physically meaningful temperature nodes. This transforms the multi-field coupled heat transfer problem, which is difficult to solve directly, into a discrete problem that can be solved by the nodal energy balance equation, thereby significantly reducing computational complexity. At the same time, by performing targeted modeling of key components such as the thermocouple sensing part, the measured hot gas, the thermocouple wire, the heat shield, the rake body, and the environment, the main heat transfer paths and heat exchange characteristics between the components can be preserved, enabling this application to maintain high physical accuracy while simplifying the process.

[0056] In one embodiment of this application, constructing a thermal resistance network includes: dividing the component structure of a thermocouple rake into multiple temperature nodes; obtaining the heat transfer relationship between the multiple temperature nodes; establishing a thermal resistance module based on the heat transfer relationship between the multiple temperature nodes; and generating a thermal resistance network based on the multiple temperature nodes and the thermal resistance module, wherein the thermal resistance module includes at least one of a convective thermal resistance, a conductive thermal resistance, and a radiative thermal resistance group.

[0057] Among them, heat transfer relationship refers to the correlation of energy exchange between multiple temperature nodes through different physical mechanisms. In this embodiment, it represents the heat transfer connection between nodes through conduction, convection, and radiation. Thermocouple rake is a temperature measuring component used for temperature measurement in a high-temperature flow field. Convective thermal resistance is the equivalent thermal resistance characterizing the convective heat transfer capacity between a fluid and a solid surface. In this embodiment, it represents the heat exchange resistance determined by the fluid flow state and heat transfer coefficient. Conductive thermal resistance is the equivalent thermal resistance characterizing the heat transfer within a solid or between different solids through thermal conduction. In this embodiment, it represents the thermal conduction resistance determined by the thermal conductivity and geometric dimensions of the material. Radiation thermal resistance is the equivalent thermal resistance characterizing the energy exchange between different surfaces through thermal radiation. In this embodiment, it represents the radiation heat transfer resistance determined by factors such as surface temperature, emissivity, and viewing angle.

[0058] It is understood that the embodiments of this application abstract the complex heat transfer relationships between multiple temperature nodes in an equivalent manner, transforming the originally coupled heat conduction, convection, and radiation heat transfer processes into calculable thermal resistance modules and their connections, thereby constructing a clearly structured thermal resistance network. This transforms the complex multiphysics coupling problem into a network-based energy transfer problem, significantly reducing the difficulty of modeling and solving. Simultaneously, by classifying and modeling different heat transfer mechanisms and uniformly expressing them in the form of thermal resistance, subsequent calculations and parameter updates are facilitated while preserving the differences in various heat transfer characteristics. Furthermore, this thermal resistance network, with nodes and thermal resistance as basic units, possesses good modularity and scalability, allowing for flexible adjustment of the network structure under different operating conditions or accuracy requirements. This provides a foundation for subsequent error feedback-based optimization and node addition / reduction, thereby improving the overall computational efficiency, accuracy, and engineering applicability of the temperature correction method.

[0059] In one embodiment of this application, the component structure of the thermocouple rake is divided into multiple temperature nodes, including: obtaining the heat transfer path and temperature distribution characteristics of each component in the component structure; performing segmented discretization processing on the component structure according to the heat transfer path and temperature distribution characteristics; and determining multiple temperature nodes according to the results of the segmented discretization processing.

[0060] Among them, the heat transfer path is the physical path through which heat is transferred between different components or regions. In this embodiment, it represents the energy transfer path between the measured hot gas and each structure, as well as within each structure, through heat conduction, convection, and radiation. The segmented discretization process is a method of dividing a continuous structure into several finite segments according to its heat transfer characteristics. In this embodiment, it represents the process of dividing the component structure into blocks based on the heat transfer path and temperature distribution characteristics in order to establish a discrete model.

[0061] It is understood that, by acquiring the heat transfer paths and temperature distribution characteristics of each component, this application embodiment performs targeted segmentation and discretization of the complex component structure of the thermocouple rake, transforming the continuously distributed temperature field into discrete temperature nodes with clear physical meaning. This transforms the multidimensional coupled heat transfer problem into a discrete problem solvable by the node energy balance equation, significantly reducing computational complexity. Simultaneously, by combining heat transfer paths and temperature distribution characteristics for node partitioning, unnecessary over-refinement can be avoided while preserving key heat transfer channels and main temperature gradient characteristics, achieving an effective balance between accuracy and computational efficiency in this application embodiment. Furthermore, this application provides clear physical basis for node partitioning, offering a structural foundation for subsequently constructing a thermal resistance network that uniformly represents multiple heat transfer mechanisms such as convection, conduction, and radiation.

[0062] In one embodiment of this application, generating a thermal resistance relationship network based on multiple temperature nodes and thermal resistance modules includes: determining the corresponding thermal resistance type based on the heat transfer relationship between multiple temperature nodes; performing equivalent characterization of the heat transfer path between multiple temperature nodes based on the thermal resistance type; determining the thermal resistance based on the result of the equivalent characterization, and determining the connection method between the temperature nodes and the thermal resistance; and generating a thermal resistance relationship network based on multiple temperature nodes, thermal resistance, and connection method.

[0063] Among them, thermal resistance type refers to the classification of thermal resistance according to different heat transfer mechanisms, and in this application embodiment, it represents different types of thermal resistance such as convection thermal resistance, conduction thermal resistance, and radiation thermal resistance; equivalent characterization is a processing method that approximates the actual complex heat transfer process through a simplified model, and in this application embodiment, it represents the transformation of a continuous heat transfer path into a discrete expression with equivalent thermal resistance characteristics; equivalent thermal resistance is a parameter characterizing the degree of heat transfer obstruction obtained after simplifying the actual heat transfer process, and in this application embodiment, it represents the thermal resistance value determined by the specific heat transfer path and its corresponding physical characteristics; heat transfer path is the physical path through which heat is transferred between different temperature nodes, and in this application embodiment, it represents the path through which energy exchange is achieved through conduction, convection, or radiation; connection method is the topological connection relationship between temperature nodes and thermal resistance modules, and in this application embodiment, it represents the series, parallel, or combined connection forms between nodes and thermal resistance.

[0064] It is understood that the embodiments of this application identify and equivalently characterize the heat transfer relationships between different temperature nodes, and uniformly map the complex and diverse heat transfer paths into standardized thermal resistance types, thereby achieving standardized modeling of various heat transfer mechanisms such as conduction, convection and radiation, and reducing the complexity of model construction. At the same time, by transforming the continuous heat transfer process into equivalent thermal resistance and clarifying the connection method between temperature nodes and thermal resistance, the multi-path coupled heat transfer problem that was originally difficult to solve directly is transformed into a network problem with a clear topological structure, which is convenient to solve using a unified energy balance method.

[0065] Specifically, in this embodiment, component temperature distribution data is first generated based on component temperature data to characterize the temperature changes of the thermocouple probe, thermocouple wire, heat shield, and mounting base at different spatial locations, thereby reflecting the differences in heat transfer between components. Based on this, a thermal resistance network is constructed, the principle of which is as follows: Figure 6 As shown.

[0066] The thermocouple rake temperature measurement system includes components such as a thermocouple rake body, a thermocouple, a heat shield, and the measured hot gas. The thermocouple rake body corresponds to the rake body node in this application, the thermocouple corresponds to the thermocouple node and its sensing part node, the heat shield corresponds to the heat shield node, and the measured hot gas corresponds to the measured hot gas node. The above components are coupled through airflow and heat transfer processes, and together they constitute the actual physical system for temperature measurement, and serve as the physical basis for the subsequent construction of the thermal resistance network.

[0067] Regarding the airflow distribution, the measured hot gas enters from the inlet of the measurement area with a certain incoming flow velocity, temperature and pressure, and forms a complex flow structure around the thermocouple and heat shield. Some of the airflow flows out along the top and side of the measurement area, corresponding to the incoming flow conditions and flow field distribution characteristics in this application. This flow process determines the convective heat transfer intensity of the thermocouple probe and the surface of the heat shield, and provides a basis for the subsequent determination of convective thermal resistance.

[0068] In terms of heat transfer mechanisms, the thermocouple rake temperature measurement system contains multiple heat transfer paths, including conduction, convection, and radiation. Specifically, the heat transfer between the thermocouple wire and the rake body corresponds to the conduction process, the heat exchange between the measured hot gas and the thermocouple probe and heat shield corresponds to the convection process, and the heat exchange between the heat shield and the thermocouple, as well as between the heat shield and the external environment, corresponds to the radiation process. These multiple heat transfer mechanisms are coupled with each other and jointly affect the temperature distribution of the thermocouple sensing part and the measurement results.

[0069] Based on the above heat transfer paths, the heat transfer relationships between multiple temperature nodes are identified, and the corresponding thermal resistance types, including conductive thermal resistance, convective thermal resistance, and radiative thermal resistance, are determined according to different heat transfer mechanisms. On this basis, each heat transfer path is equivalently characterized, and the continuous heat transfer process is transformed into discrete equivalent thermal resistance parameters. Based on the actual physical connection relationship between each node, the connection method between the temperature node and the thermal resistance is determined, thereby forming a thermal resistance relationship network with a clear topological structure.

[0070] After obtaining the correlation of the convective heat transfer coefficient and the flow coefficient, this application constructs a lumped-parameter thermal resistance network model based on the thermal resistance relation network, and its simplified topology is as follows: Figure 7 As shown, this model discretizes the component structure into a finite number of temperature nodes based on the non-uniformity of temperature distribution in the same component, and establishes corresponding thermal resistance connections between each node.

[0071] Furthermore, each thermal resistance module is correlated with the actual flow rate and the convective heat transfer coefficient to characterize the convective heat transfer process between the fluid and the surface of each component, as well as the influence of flow conditions on heat transfer characteristics, thereby achieving unified modeling and coupled calculation of multiple heat transfer mechanisms such as heat conduction, convective heat transfer, and thermal radiation.

[0072] In step S104, the temperature nodes and thermal resistance modules in the thermal resistance network are adjusted based on the second error to obtain the thermal resistance network and heat transfer process correlation correction coefficient that match the computational fluid dynamics simulation data. The second error is the numerical difference between the computational fluid dynamics simulation data and the output data of the thermal resistance network model under the preset inflow conditions.

[0073] The second error is an evaluation index used to characterize the degree of deviation between the calculation results of the thermal resistance network model and the reference data. In this embodiment, it represents the error value between the thermocouple total temperature rake measurement error data obtained by computational fluid dynamics simulation and the thermocouple total temperature rake measurement error data calculated by the thermal resistance network. Adjustment in this embodiment represents improving the resolution capability of the thermal resistance network model by increasing the number of temperature nodes and thermal resistance modules. The number of temperature nodes and thermal resistance modules in the thermal resistance network is a parameter characterizing the complexity of the model structure. In this embodiment, it represents the node size index used to describe the discreteness and calculation accuracy of the thermal resistance network.

[0074] This application embodiment introduces an error comparison and iterative correction mechanism between simulation data and the output results of the thermal resistance network model. This enables the thermal resistance network model to be consistent with actual flow heat transfer characteristics, improving the model's calculation accuracy. When the second error exceeds a preset second threshold, temperature nodes and thermal resistance modules are adaptively added to achieve local model refinement and structural optimization, thereby improving the modeling accuracy and stability under complex temperature field conditions. Ultimately, the thermal resistance network model obtains higher accuracy temperature correction results under convergence conditions, improving the reliability of thermocouple rake temperature measurement correction. The preset second threshold is a preset standard used to determine whether the model's calculation accuracy meets the requirements; in this application embodiment, it represents the upper limit of allowable error set according to engineering application needs.

[0075] Specifically, the embodiments of this application first obtain computational fluid dynamics simulation data under preset inflow conditions and compare it with temperature measurement error correction data output by the thermal resistance network model. A quantitative evaluation system with the second error as the core is established, which transforms the model accuracy from empirical judgment to a quantifiable and determinable evaluation process, thereby improving the objectivity and repeatability of model verification. Secondly, when the second error exceeds the preset second threshold, the thermal resistance network model structure is refined by expanding the number of temperature nodes and thermal resistance modules in the thermal resistance relationship network. This allows the local temperature gradient, complex heat transfer path and multi-mechanism coupling effect, which were originally difficult to describe accurately, to be more fully characterized, thereby effectively reducing the structural error caused by the model being too coarsely discretized. Furthermore, this application does not increase the complexity of the thermal resistance relationship network and thermal resistance network model all at once, but rather gradually increases the number of nodes driven by error, achieving on-demand growth in complexity. This ensures that the investment of computing resources matches the improvement in accuracy and avoids unnecessary redundant calculations. At the same time, by setting a second threshold as a convergence criterion, the optimization process of this application embodiment has a clear termination condition, ensuring that iteration stops in a timely manner while meeting accuracy requirements. This allows for control of the computational scale and computation time while ensuring computational accuracy. In addition, this application embodiment complements the aforementioned parameter calibration process based on experimental data, optimizing from the two dimensions of "parameter accuracy" and "structural rationality," respectively. This constructs a dual optimization mechanism for the coordinated convergence of parameters and structure, enabling the thermal resistance network model to maintain stable and reliable predictive performance under different incoming flow conditions. Ultimately, through the above mechanism, this application achieves the expected accuracy level before performing actual temperature correction calculations, thereby significantly improving the overall computational reliability, engineering applicability, and multi-condition response capability of the method.

[0076] In step S105, the measurement temperature correction value of the thermocouple rake is calculated using a thermal resistance network model.

[0077] The measured temperature correction value is a result that is closer to the true incoming flow temperature after correcting the actual measured temperature of the thermocouple. In this embodiment, it represents the corrected temperature corresponding to the thermocouple sensing part calculated by the thermal resistance network model.

[0078] Understandably, calculating the temperature correction value of the thermocouple rake using a thermal resistance network model can quantify the temperature measurement errors caused by factors such as convective heat transfer, radiative heat transfer, and solid thermal conduction. This effectively eliminates the temperature deviation caused by structural thermal conduction, radiative heat dissipation, and uneven flow field heat transfer in actual measurements of the thermocouple rake, correcting the apparent measured temperature to a value closer to the actual airflow temperature, and significantly improving the accuracy and reliability of temperature measurement under high temperature and complex flow field conditions.

[0079] In one embodiment of this application, the method for calculating the measurement temperature correction value of a thermocouple rake using a thermal resistance network model includes: obtaining arbitrary incoming flow conditions for the thermocouple rake; inputting the arbitrary incoming flow conditions into the thermal resistance network model, and outputting the temperature values ​​and flow velocity parameters of each temperature node through the thermal resistance network model; extracting the temperature value of the sensing part of the thermocouple in the temperature node, and using the temperature value of the sensing part of the thermocouple as the measurement temperature correction value of the thermocouple rake.

[0080] In this application, "arbitrary inflow conditions" refers to the fluid inlet state parameters that may occur during actual operation. In this embodiment, it represents any combination of inflow velocity, inflow temperature, and inflow pressure under different operating conditions within the thermocouple rake. The temperature values ​​of the temperature nodes are the temperature results corresponding to each discrete node calculated by the thermal resistance network model. In this embodiment, they represent the equivalent temperature of each component or region obtained by solving the node energy balance equation. The flow velocity parameters of the temperature nodes are the flow state parameters corresponding to each discrete node calculated by the thermal resistance network model. In this embodiment, they represent the local flow velocity or equivalent flow velocity obtained based on the flow coefficient, flow continuity relationship, and flow heat transfer relationship between nodes. The temperature value of the thermocouple's sensing part is the node temperature corresponding to the thermocouple's temperature-sensitive area. In this embodiment, it represents the node calculation results in the thermal resistance network model used to characterize the actual temperature measurement location of the thermocouple.

[0081] Understandably, by inputting arbitrary incoming flow conditions into a calibrated and optimized thermal resistance network model, a rapid mapping from operating parameters to temperature distribution results is achieved, significantly improving computational efficiency and response speed. Simultaneously, by outputting the temperature values ​​and flow velocity parameters of all temperature nodes, the internal temperature distribution of the measurement component structure can be fully reflected, providing a basis for analyzing the heat transfer relationships between components and the sources of error. Furthermore, by extracting the temperature corresponding to the thermocouple sensing part as a correction value for the measured temperature, the correction result directly corresponds to the actual temperature measurement location, avoiding additional errors caused by indirect calculation.

[0082] In one embodiment of this application, the temperature values ​​and flow velocity parameters of each temperature node are output through a thermal resistance network model, including: calculating the actual flow velocity under any incoming flow conditions based on the flow coefficient; calculating the convective heat transfer coefficient under any incoming flow conditions based on the correlation formula of the convective heat transfer coefficient; and calculating the total temperature and static temperature of each temperature node based on the actual flow velocity, flow coefficient, convective heat transfer coefficient and thermal resistance network.

[0083] Wherein, actual flow velocity is the true flow velocity of the fluid in a local area after considering the influence of flow resistance. In this embodiment, it represents the flow velocity parameter that reflects the true flow state near the thermocouple probe and the heat shield after being corrected by the flow coefficient; total temperature is the total stagnation temperature of the incoming high-speed airflow containing the flow kinetic energy, representing the input temperature of the heat transfer boundary on the incoming side; static temperature is the actual thermodynamic temperature in this embodiment, which is the basic temperature for thermal resistance heat transfer calculation.

[0084] It is understood that the embodiments of this application introduce the correlation between the flow coefficient and the convective heat transfer coefficient into the specific calculation process, thereby realizing the dynamic correction of the actual flow and heat transfer state and avoiding the problem of insufficient applicability caused by using fixed parameters.

[0085] First, by correcting the incoming flow velocity using the flow coefficient, the flow resistance effect caused by the thermocouple rake and heat shield can be effectively reflected, making the calculated actual flow velocity closer to the real flow state, thereby improving the accuracy of subsequent heat transfer calculations. Second, by calculating the convective heat transfer coefficient under the current operating condition using the correlation formula, the heat transfer capacity can be dynamically adjusted with changes in velocity, temperature, and pressure, thus more realistically reflecting the convective heat transfer characteristics under different operating conditions. Furthermore, the actual flow velocity and the convective heat transfer coefficient are used together in the calculation of the thermal resistance network, allowing multiple heat transfer mechanisms such as convection, conduction, and radiation to be coupled and solved within a unified framework, thereby obtaining the temperature values ​​and flow velocity parameters at each temperature node. At the same time, this step expresses the complex flow and heat transfer effects equivalently through a few key parameters, significantly reducing computational complexity while ensuring physical accuracy, achieving an effective balance between computational efficiency and accuracy. As a result, the model can quickly and stably output high-precision temperature distribution results under multiple operating conditions, improving the overall engineering applicability and reliability of the method.

[0086] Specifically, in one embodiment of this application, during real-time calculation, for any given incoming flow conditions, including incoming flow velocity, temperature and pressure, the incoming flow velocity is first corrected based on a pre-calibrated flow coefficient to obtain the actual flow velocity near the thermocouple probe; at the same time, the convective heat transfer coefficient under the corresponding operating condition is calculated based on the correlation formula of the convective heat transfer coefficient and combined with the current incoming flow conditions.

[0087] Subsequently, the actual flow velocity and convective heat transfer coefficient were introduced as key input parameters into the thermal resistance network model. Based on multiple temperature nodes and the thermal resistance relationships between them, the thermal behavior of the measurement system was uniformly solved by establishing energy balance equations for each node. During the solution process, the coupling effects of multiple heat transfer mechanisms were comprehensively considered, including heat conduction between different nodes within the same component, convective heat transfer between the fluid and surfaces such as thermocouple probes and heat shields, and thermal radiation between high-temperature components and combustion chamber walls or other low-temperature environments. These heat transfer processes were then modeled as corresponding thermal resistances in a unified manner.

[0088] By solving the energy balance equation, the temperature values ​​and flow velocity parameters of each temperature node in the thermal resistance network model can be obtained. Specifically, the temperature value of the temperature node corresponding to the thermocouple sensing part is extracted as the correction value for the measured temperature of the thermocouple rake, thereby achieving a high-precision mapping from the thermocouple indicated temperature to the actual incoming flow temperature.

[0089] Therefore, this embodiment establishes a direct calculation path from the incoming flow conditions to the actual temperature. Without having to repeatedly carry out high-cost numerical simulations, it can quickly obtain high-precision temperature correction results under varying operating conditions, thus balancing computational efficiency and result reliability.

[0090] According to the temperature correction method for thermocouple rake measurement using a thermal resistance network proposed in this application, firstly, a computational fluid dynamics (CFD) model is established, and numerical simulations are performed under multiple preset inflow conditions to obtain initial CFD data. The CFD model is then corrected based on a first error between the calibration test measurement data and the initial CFD data under the preset inflow conditions, to obtain a numerical method and turbulence model that match the calibration test measurement data, thereby obtaining CFD simulation data. Secondly, a thermal resistance network including multiple temperature nodes and thermal resistance modules is constructed, and a thermal resistance network model is built based on the correlations of convective heat transfer coefficients, radiation intensity, flow coefficients, and thermal resistance relationships. Then, based on a second error between the CFD simulation data and the output data of the thermal resistance network model under the preset inflow conditions, the temperature nodes and thermal resistance modules in the thermal resistance network are adjusted to obtain a thermal resistance network and heat transfer process correlation correction coefficients that match the CFD simulation data. Finally, the temperature correction value of the thermocouple rake is calculated using the thermal resistance network model. In summary, this application, based on the principle of similarity, extracts corresponding dimensionless quantities, which can significantly reduce the number of operating conditions in experiments and numerical simulations. This reduces the experimental costs and numerical simulation time costs required for training the thermal resistance network model. Simultaneously, it balances the advantages of experiments, simulations, and thermal resistance networks, achieving the beneficial effects of low cost in calibrating numerical models, short training time in numerical simulations, and high accuracy in thermal resistance network calculations. Therefore, it solves the problems of related technologies that combine heat shields and computational fluid dynamics to obtain correction parameters, resulting in high computational costs, reliance on empirical correlations, and insufficient measurement accuracy.

[0091] Next, the thermal resistance network thermocouple rake temperature measuring device according to the embodiments of this application is described with reference to the accompanying drawings.

[0092] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0093] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0094] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0095] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0096] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for correcting temperature measurement using a thermocouple rake with a thermal resistance network, characterized in that, Includes the following steps: Establish a computational fluid dynamics model and perform numerical simulations under multiple preset inflow conditions to obtain initial computational fluid dynamics data; The computational fluid dynamics model is corrected based on the first error to obtain a numerical method and a turbulence model that match the calibration test measurement data. Numerical simulation is performed under preset inflow conditions based on the numerical method and the turbulence model to obtain computational fluid dynamics simulation data. The first error is the numerical difference between the calibration test measurement data under the preset inflow conditions and the initial computational fluid dynamics data. A thermal resistance network is constructed, which includes multiple temperature nodes and thermal resistance modules. A thermal resistance network model is constructed based on the correlation of convective heat transfer coefficient, radiation intensity correlation, flow coefficient and the thermal resistance network. Based on the second error, the temperature nodes and thermal resistance modules in the thermal resistance network are adjusted. When the second error exceeds a preset threshold, multiple temperature nodes are determined according to the segmented discrete processing result of the component structure of the thermocouple rake. The number of temperature nodes and thermal resistance modules is increased until the second error meets the preset accuracy requirement. A thermal resistance network and heat transfer process correlation correction coefficient that match the computational fluid dynamics simulation data are obtained. The second error is the numerical difference between the computational fluid dynamics simulation data and the output data of the thermal resistance network model under the preset inflow conditions. The temperature correction value of the thermocouple rake is calculated using the thermal resistance network model.

2. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 1, characterized in that, The acquisition of initial computational fluid dynamics data includes: A computational fluid dynamics model is established based on the geometric structure data of the thermocouple sensing part, thermocouple wire, heat shield and mounting base in the thermocouple rake. The flow field and temperature of the thermocouple rake are simulated by computational fluid dynamics under multiple preset inflow conditions to obtain velocity field distribution data and temperature field distribution data. Based on the velocity field distribution data and the temperature field distribution data, calculate the correlation formulas for convective heat transfer coefficient, radiation intensity, and flow coefficient.

3. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 1, characterized in that, The temperature node includes the sensing part of the thermocouple and other nodes, which include at least one of the measured hot gas, thermocouple wire, heat shield, rake body and environment.

4. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 1, characterized in that, The construction of the thermal resistance network includes: The component structure of the thermocouple rake is divided into multiple temperature nodes; Obtain the heat transfer relationship between multiple temperature nodes; A thermal resistance module is established based on the heat transfer relationship between the multiple temperature nodes. A thermal resistance relationship network is generated based on the multiple temperature nodes and the thermal resistance module, wherein the thermal resistance module includes at least one of a convective thermal resistance, a conductive thermal resistance, and a radiative thermal group.

5. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 4, characterized in that, The process of dividing the component structure of the thermocouple rake into multiple temperature nodes includes: Obtain the heat transfer path and temperature distribution characteristics of each component in the component structure; The component structure is segmented and discretized according to the heat transfer path and temperature distribution characteristics. Multiple temperature nodes are determined based on the results of the segmented discretization process.

6. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 4, characterized in that, The step of generating a thermal resistance relationship network based on the plurality of temperature nodes and the thermal resistance module includes: The corresponding thermal resistance type is determined based on the heat transfer relationship between multiple temperature nodes. Based on the aforementioned thermal resistance type, the heat transfer path between multiple temperature nodes is equivalently characterized. The thermal resistance is determined based on the results of the equivalent characterization, and the connection method between the temperature node and the thermal resistance is determined. A thermal resistance relationship network is generated based on the multiple temperature nodes, the thermal resistance, and the connection method.

7. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 1, characterized in that, The preset inflow conditions include different combinations of inflow velocity, inflow temperature, and inflow pressure.

8. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 1, characterized in that, The calculation of the temperature correction value of the thermocouple rake using the thermal resistance network model includes: Obtain any incoming flow conditions for the thermocouple rake; The arbitrary incoming flow conditions are input into the thermal resistance network model, and the temperature value and flow velocity parameters of each temperature node are output through the thermal resistance network model. Extract the temperature value of the sensing part of the thermocouple in the temperature node, and use the temperature value of the sensing part of the thermocouple as the measurement temperature correction value of the thermocouple rake.

9. The method for correcting temperature measurement using a thermocouple rake with a thermal resistance network according to claim 8, characterized in that, The step of outputting the temperature values ​​and flow velocity parameters of each temperature node through the thermal resistance network model includes: Calculate the actual flow velocity under any incoming flow conditions based on the flow coefficient; The convective heat transfer coefficient under any incoming flow condition is calculated based on the aforementioned convective heat transfer coefficient correlation. The total temperature and static temperature of each temperature node are calculated based on the actual flow velocity, flow coefficient, convective heat transfer coefficient, and thermal resistance network.

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