High temperature surface contact resistance elimination method based on double thermocouple compensation

Abstract

The application discloses a high-temperature surface contact thermal resistance elimination method based on double thermocouple compensation, and belongs to the technical field of material thermal performance characterization and testing. The method comprises the following steps: synchronously collecting double thermocouple temperatures, obtaining a temperature difference signal through filtering, calibration and environment correction; calculating a real-time heat flow density according to the corrected temperature difference, the Seebeck coefficient and a dynamic correction factor; establishing a nonlinear dynamic coupling model containing a contact thermal resistance parameter, and dividing a temperature interval; online identifying the contact thermal resistance parameter by using an adaptive recursive least square algorithm, and generating a dynamic thermal resistance compensation value; and applying the compensation value to the double thermocouple signals according to a weight, and outputting an accurate high-temperature surface temperature. The method adopts a data acquisition and preprocessing strategy, a dynamic heat flow correction strategy, a nonlinear segmented modeling strategy, an adaptive online identification strategy and a refined compensation output strategy, can effectively eliminate the influence of high-temperature surface contact thermal resistance, and significantly improves the accuracy and reliability of temperature measurement under a high-temperature environment.

Description

Technical Field

[0001] This invention relates to the field of material thermal property characterization and testing technology, and in particular to a method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation. Background Technology

[0002] In numerous industrial fields such as aerospace, metallurgy, and energy chemicals, accurate measurement of the surface temperature of materials or key components under high-temperature conditions is crucial for ensuring product quality, optimizing process parameters, and ensuring safe equipment operation. Contact temperature measurement, especially using thermocouples, is widely used due to its simple structure and wide temperature range. However, when a thermocouple comes into contact with the high-temperature surface being measured, an additional heat flow barrier, known as contact thermal resistance, exists because the interface between the two is not an ideal perfect contact. In the study of high-temperature material properties, quantifying contact thermal resistance is a prerequisite for accurately obtaining key parameters such as thermal conductivity and thermal diffusivity. Traditional thermal testing methods assume that contact thermal resistance is zero or constant, but in actual high-temperature dynamic environments, the nonlinear changes in contact thermal resistance can significantly affect the accuracy of evaluating the true thermal properties of materials.

[0003] Currently, high-temperature surface contact temperature measurement mainly uses direct measurement with a single thermocouple. However, the measured values ​​inevitably contain significant errors introduced by contact thermal resistance, and these errors are difficult to quantify and eliminate. Although there are improved techniques, such as dual thermocouple differential compensation, which calculates and compensates for thermal resistance by analyzing the difference in readings between the two thermocouples, the effectiveness of existing dual thermocouple methods relies on the assumption that the difference in contact thermal resistance is constant or the interface state is stable. In actual high-temperature dynamic environments, factors such as oxidation, contamination, minor deformation of the measured surface, and relaxation of thermocouple contact pressure can all cause unpredictable dynamic changes in contact thermal resistance. This limits the accuracy of compensation methods based on static assumptions, and may even render them ineffective, failing to meet the requirements for high-precision measurement. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method for eliminating contact thermal resistance on high-temperature surfaces based on dual thermocouple compensation. This method employs multi-stage signal preprocessing to improve data quality, combines a dynamic model to identify real-time heat flux density, establishes a nonlinear dynamic coupling model incorporating contact thermal resistance parameters, and uses an adaptive recursive least squares algorithm to identify the contact thermal resistance parameters online. Finally, the dynamic thermal resistance compensation value is refined and applied to the temperature signal. This method can eliminate the influence of contact thermal resistance in real-time and adaptively under high-temperature dynamic environments, significantly improving the accuracy and reliability of high-temperature surface temperature measurement.

[0005] The above objectives can be achieved through the following approach:

[0006] A method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation includes: simultaneously acquiring the real-time temperature values ​​of a first thermocouple and a second thermocouple to obtain a first temperature signal and a second temperature signal; calculating the real-time temperature difference between the first temperature signal and the second temperature signal to obtain a temperature difference signal; calculating the real-time heat flux density at the contact interface based on the temperature difference signal and the Seebeck coefficient of the thermocouples; establishing a dynamic coupling relationship model between the real-time heat flux density and the real-time temperature difference, wherein the dynamic coupling relationship model includes contact thermal resistance parameters; using a recursive least squares algorithm to identify the contact thermal resistance parameters in the dynamic coupling relationship model online, generating dynamic thermal resistance compensation values; using the dynamic thermal resistance compensation values ​​to synchronously compensate and correct the first temperature signal and the second temperature signal, and outputting the compensated and corrected high-temperature surface temperature measurement results.

[0007] Optionally, calculating the real-time temperature difference between the first temperature signal and the second temperature signal includes: performing real-time digital filtering on the first temperature signal and the second temperature signal respectively to eliminate random noise interference, obtaining a filtered first temperature signal and a filtered second temperature signal; performing temperature dependence calibration on the filtered first temperature signal and the filtered second temperature signal according to the respective temperature response characteristics of the first thermocouple and the second thermocouple; and calculating the real-time difference between the calibrated first temperature signal and the calibrated second temperature signal to obtain the temperature difference signal.

[0008] Optionally, the calculation of the real-time heat flux density at the contact interface includes: obtaining the real-time ambient temperature of the environment where the contact interface is located; correcting the temperature difference signal for the ambient temperature based on the temperature drift characteristics of the first thermocouple and the second thermocouple to obtain a corrected temperature difference signal; initially calculating the real-time heat flux density based on the corrected temperature difference signal and the Seebeck coefficient of the thermocouple to obtain a preliminary heat flux density; and correcting the preliminary heat flux density by introducing a dynamic correction factor based on the preliminary heat flux density and the thermal response hysteresis characteristics of the thermocouple in a high-temperature environment to obtain the final real-time heat flux density at the contact interface.

[0009] Optionally, establishing the dynamic coupling relationship model between the real-time heat flux density and the real-time temperature difference includes: dividing the high-temperature surface temperature measurement range into several preset temperature intervals; for each temperature interval, establishing a nonlinear dynamic coupling sub-model including contact thermal resistance parameters, wherein the nonlinear dynamic coupling sub-model characterizes the nonlinear relationship between heat flux density and temperature difference within that temperature interval; and automatically selecting and applying the nonlinear dynamic coupling sub-model for the corresponding temperature interval based on the current real-time temperature value to obtain the dynamic coupling relationship model between the real-time heat flux density and the real-time temperature difference.

[0010] Optionally, the online identification of contact thermal resistance parameters in the dynamic coupling model using the recursive least squares algorithm includes: initializing the parameter set of the recursive least squares algorithm based on the measured data of real-time heat flux density and real-time temperature difference; calculating the residual between the predicted output of the dynamic coupling model and the actual measured value in real time, and adaptively adjusting the forgetting factor in the recursive least squares algorithm according to the magnitude of the residual; iteratively updating the contact thermal resistance parameters to a preset convergence condition or achieving stable identification accuracy using the adjusted forgetting factor and the recursive least squares algorithm to obtain dynamic thermal resistance compensation values.

[0011] Optionally, using the dynamic thermal resistance compensation value to synchronously compensate and correct the first temperature signal and the second temperature signal includes: determining the allocation weight of the dynamic thermal resistance compensation value on the first temperature signal and the second temperature signal based on the distance and thermal contact characteristics of the first thermocouple and the second thermocouple to the high-temperature surface, respectively; applying the dynamic thermal resistance compensation value to the first temperature signal and the second temperature signal respectively according to the allocation weight to compensate and correct them, thereby obtaining the compensated and corrected first temperature signal and the compensated and corrected second temperature signal; selecting and outputting the temperature value that best matches the preset measurement target from the compensated and corrected first temperature signal and the compensated and corrected second temperature signal as the high-temperature surface temperature measurement result, wherein the preset measurement target is the actual surface temperature or the temperature at a certain depth.

[0012] Optionally, the correction by introducing a dynamic correction factor includes: acquiring the operating temperature and ambient temperature of the first and second thermocouples in real time, and dynamically calculating the transient response time constant of the thermocouples under the current operating conditions based on historical data; using the Kalman filter algorithm to iteratively estimate and update the dynamic correction factor based on the transient response time constant; and obtaining the final real-time heat flux density at the contact interface based on the estimated and updated dynamic correction factor and the preliminary heat flux density.

[0013] Optionally, the adaptive adjustment of the forgetting factor in the recursive least squares algorithm based on the magnitude of the residual includes: determining a preset threshold for the residual based on the historical running data of the dynamic coupling relationship model, including an upper threshold and a lower threshold; when the absolute value of the residual exceeds the upper threshold, reducing the value of the forgetting factor to improve the response speed of the recursive least squares algorithm to new data; when the absolute value of the residual is lower than the lower threshold, increasing the value of the forgetting factor to improve the convergence stability and noise resistance of the recursive least squares algorithm; and when the absolute value of the residual is between the lower threshold and the upper threshold, maintaining the value of the forgetting factor unchanged to maintain the balance of identification.

[0014] Optionally, determining the allocation weight of the dynamic thermal resistance compensation value on the first temperature signal and the second temperature signal includes: establishing a local thermal resistance distribution model between the thermocouple and the high-temperature surface based on the position and geometric dimensions of the first and second thermocouples, as well as the material properties of the high-temperature surface; dynamically calculating the heat-affected zone of each of the first and second thermocouples and their contribution to the overall contact thermal resistance based on the local thermal resistance distribution model and in conjunction with the real-time heat flux density; and determining the allocation weight of the dynamic thermal resistance compensation value on the first temperature signal and the second temperature signal based on the contribution.

[0015] Based on the same inventive concept, this invention also provides a high-temperature surface contact thermal resistance elimination system based on dual thermocouple compensation. The system includes: a temperature acquisition module for simultaneously acquiring real-time temperature values ​​of a first thermocouple and a second thermocouple to obtain a first temperature signal and a second temperature signal; a temperature difference calculation module for calculating the real-time temperature difference between the first temperature signal and the second temperature signal to obtain a temperature difference signal; a heat flux density calculation module for calculating the real-time heat flux density at the contact interface based on the temperature difference signal and the Seebeck coefficient of the thermocouples; a dynamic coupling model establishment module for establishing a dynamic coupling relationship model between the real-time heat flux density and the real-time temperature difference, wherein the model includes contact thermal resistance parameters; an online thermal resistance parameter identification module for using a recursive least squares algorithm to identify the contact thermal resistance parameters in the dynamic coupling relationship model online, generating dynamic thermal resistance compensation values; and a temperature compensation correction and output module for using the dynamic thermal resistance compensation values ​​to synchronously compensate and correct the first temperature signal and the second temperature signal, outputting the compensated and corrected high-temperature surface temperature measurement result.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. This invention significantly improves the signal-to-noise ratio and accuracy of the original temperature and temperature difference signals by introducing multi-level signal preprocessing, including real-time digital filtering, temperature-dependent calibration, and ambient temperature correction. This overcomes the drawbacks of traditional thermocouple measurements being susceptible to noise and environmental drift interference, providing a high-quality input data foundation for subsequent heat flux density calculation and contact thermal resistance identification, thereby ensuring the reliability of high-temperature surface temperature measurement from the source.

[0018] 2. This invention innovatively establishes a nonlinear dynamic coupling model incorporating contact thermal resistance parameters and introduces a dynamic correction factor based on thermal response hysteresis characteristics. Online identification is then performed using an adaptive recursive least squares algorithm. This enables the identification process of thermal resistance parameters to adapt in real-time to the complex dynamic changes in high-temperature environments, breaking the limitations of existing static compensation methods and significantly improving the accuracy and dynamic adaptability of contact thermal resistance identification. This directly serves the accurate testing of material thermal properties under high-temperature dynamic environments.

[0019] 3. This invention achieves refinement and intelligence in the compensation and correction process. By determining the allocation weight of the dynamic thermal resistance compensation value based on the distance between the thermocouple and the high-temperature surface and the thermal contact characteristics, and selecting the most suitable temperature value for output based on the preset measurement target, this strategy ensures that the compensation value can more accurately act on the actual temperature signal affected by thermal resistance, effectively eliminating the non-uniform influence of contact thermal resistance. The final output temperature measurement result highly matches the actual high-temperature surface temperature, greatly improving the measurement accuracy and practical application value.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart of a high-temperature surface contact thermal resistance elimination method based on dual thermocouple compensation according to an embodiment of the present invention.

[0023] Figure 2 This is a comparison diagram of the original temperature signal before and after filtering and calibration according to an embodiment of the present invention.

[0024] Figure 3 This is an online convergence curve of the contact thermal resistance parameter and a dynamic adjustment diagram of the forgetting factor in an embodiment of the present invention.

[0025] Figure 4 This is a graph showing the variation of local contact thermal resistance with temperature under different contact conditions according to an embodiment of the present invention.

[0026] Figure 5 This is a schematic diagram of the high-temperature surface contact thermal resistance elimination system based on dual thermocouple compensation according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 One embodiment of the present invention proposes a method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation. This method employs data acquisition and preprocessing, dynamic heat flow correction, nonlinear piecewise modeling, adaptive online identification, and refined compensation output strategies, which can effectively eliminate the influence of high-temperature surface contact thermal resistance and significantly improve the accuracy and reliability of temperature measurement in high-temperature environments.

[0029] The method described in this embodiment specifically includes:

[0030] The real-time temperature values ​​of the first thermocouple and the second thermocouple are collected simultaneously to obtain the first temperature signal and the second temperature signal.

[0031] Specifically, a first thermocouple (TC1) and a second thermocouple (TC2) are placed at the high-temperature surface. The measuring ends of the two thermocouples are kept in contact with the high-temperature surface. The millivolt voltage signals of TC1 and TC2 are synchronously read at a set sampling frequency using a high-precision data acquisition card (DAC) or a dedicated temperature measuring instrument, and converted into corresponding real-time temperature values. These real-time temperature values ​​are then converted into digital signal streams, namely the first temperature signal and the second temperature signal.

[0032] Calculate the real-time temperature difference between the first temperature signal and the second temperature signal to obtain the temperature difference signal;

[0033] Specifically, the temperature values ​​of the first and second temperature signals acquired synchronously are subtracted point by point at each sampling time. The real-time temperature value of the first temperature signal minus the real-time temperature value of the second temperature signal forms a real-time changing temperature difference sequence, which constitutes the temperature difference signal.

[0034] The real-time heat flux density at the contact interface is calculated based on the temperature difference signal and the Seebeck coefficient of the thermocouple.

[0035] Specifically, the real-time heat flux density can be calculated based on the temperature difference signal and the Seebeck coefficient of the thermocouple material. This calculation takes into account the thermocouple's structure and dimensions, and is derived using a thermocouple heat conduction model. This heat conduction model describes the relationship between temperature difference and heat flux density, thus yielding the real-time heat flux density at the contact interface.

[0036] A dynamic coupling model is established between the real-time heat flux density and the real-time temperature difference, wherein the dynamic coupling model includes contact thermal resistance parameters;

[0037] Specifically, the dynamic coupling model aims to describe the dynamic relationship between real-time heat flux density and real-time temperature difference during dynamic heat transfer, and explicitly includes the contact thermal resistance parameter. This model can be a transfer function model, a state-space model, or an autoregressive exogenous input model. The coefficients in the model are correlated with the contact thermal resistance parameter, allowing this parameter to be identified using both the input (temperature difference) and output (heat flux density) data.

[0038] The contact thermal resistance parameters in the dynamic coupling relationship model are identified online using a recursive least squares algorithm to generate dynamic thermal resistance compensation values.

[0039] Specifically, the Recursive Least Squares (RLS) algorithm is a commonly used online parameter identification method. This algorithm continuously updates and corrects the estimated values ​​of contact thermal resistance parameters in the dynamic coupling model using newly acquired real-time heat flux density and real-time temperature difference data at each moment. The RLS algorithm iteratively updates parameters by minimizing the sum of squared prediction errors; its core lies in the recursive calculation of the covariance matrix and parameter vector. After each iteration, the algorithm outputs the estimated contact thermal resistance parameter value at the current moment, i.e., the dynamic thermal resistance compensation value.

[0040] The dynamic thermal resistance compensation value is used to synchronously compensate and correct the first temperature signal and the second temperature signal, and the high-temperature surface temperature measurement result after compensation and correction is output.

[0041] Specifically, the dynamic thermal resistance compensation value obtained online is fed back to the temperature measurement chain. This compensation value is used to correct the first temperature signal, the second temperature signal, or a combination of both. The original measured temperature value is corrected through a preset compensation function. Finally, the temperature value after this compensation correction is output as the actual measurement result of the high-temperature surface for system monitoring, control, or data recording.

[0042] This invention employs dual thermocouples to synchronously acquire and calculate temperature differences, and combines the Seebeck coefficient of the thermocouples to estimate real-time heat flux density. A dynamic coupling relationship model including contact thermal resistance parameters is established, and the dynamic thermal resistance compensation value is identified online through a recursive least squares algorithm. Finally, this compensation value is used to synchronously correct the temperature signal. This invention can effectively quantify and eliminate the influence of contact thermal resistance introduced by imperfect contact between the thermocouple and the measured surface under high-temperature conditions, significantly improving the accuracy, real-time performance, and reliability of surface temperature measurement under high-temperature conditions.

[0043] Optionally, calculating the real-time temperature difference between the first temperature signal and the second temperature signal includes:

[0044] The first temperature signal and the second temperature signal are subjected to real-time digital filtering to eliminate random noise interference, resulting in filtered first temperature signal and filtered second temperature signal.

[0045] Specifically, digital filtering aims to effectively remove random noise and interference from the original temperature signal, thereby significantly improving signal purity and signal-to-noise ratio. This process smooths the temperature data by applying specific digital algorithms, such as smoothing average filtering, median filtering, and complex adaptive filtering methods. Choosing an appropriate filtering method is crucial to ensuring the accuracy of subsequent heat flux density calculations and contact thermal resistance distortion, as it provides high-quality temperature input unaffected by transient noise from the source.

[0046] like Figure 2 As shown, this illustrates the comparison between the original temperature signal before and after filtering and calibration.

[0047] For example, to eliminate glitches and noise caused by environmental electromagnetic interference or sensor imbalance, a real-time smoothing filter can be used. This filter updates the current temperature value by calculating the accuracy of the most recent N sampling points, thus smoothing the signal. This method effectively suppresses high-frequency noise, making the filtered temperature signal more stable and reliable, laying the foundation for subsequent processing.

[0048] Based on the respective temperature response characteristics of the first and second thermocouples, temperature-dependent calibration is performed on the filtered first temperature signal and the filtered second temperature signal.

[0049] Specifically, the thermoelectric potential output of a thermocouple is not a simple linear relationship with temperature; its Seebeck coefficient changes with temperature, and different individual thermocouples may also have manufacturing errors and differences in response characteristics. Therefore, to obtain high-precision temperature measurements, the temperature signal after parameter calibration must be calibrated. This is typically done using methods such as lookup tables, paired methods, or piecewise linear interpolation, based on the thermocouple's standard reference curve or adjustment data obtained through prior precise calibration, to eliminate system coordinates introduced by the thermocouple's inherent nonlinearity and individual errors.

[0050] For example, simply put, for a specific type of thermocouple, the relationship between its thermoelectric potential and the actual temperature T can be estimated using a high-order polynomial function. The goal of the adjustment is to establish an inverse mapping, that is, to calculate the accurate temperature value based on the measured thermoelectric potential. For example, this can be done using a polynomial of the following form:

[0051] ,

[0052] in, It is the precise temperature value after it has been determined. It is the thermoelectric potential signal (or equivalent temperature signal) output by the thermocouple. The coefficients are calculated using optimization algorithms such as the least squares method after bias calibration experiments on the thermocouple. By applying this iterative formula, the nonlinear response of the thermocouple can be effectively linearized, thereby obtaining more accurate temperature measurement results.

[0053] The temperature difference signal is obtained by calculating the real-time difference between the calibrated first temperature signal and the calibrated second temperature signal.

[0054] Specifically, after grouping the signals from the first and second thermocouples by temperature, the temperature values ​​at the same sampling time are subtracted to determine the relative temperatures. This is a direct arithmetic subtraction to accurately obtain the real-time temperature gradient at the contact interface between the two thermocouples. This real-time temperature difference signal is the core data for subsequently calculating the heat flux density and contact thermal resistance parameters at the contact interface. Consequently, if online recovery is performed, the measured temperature of the first thermocouple at a given moment is... At the same time, the temperature measured by the second thermocouple is The real-time temperature difference is calculated as follows:

[0055] ,

[0056] This calculation yielded The value is the temperature difference signal used for subsequent analyses and calculations.

[0057] Optionally, the calculation of the real-time heat flux density at the contact interface includes:

[0058] The real-time ambient temperature of the environment where the contact interface is located is obtained, and the temperature difference signal is corrected for ambient temperature based on the temperature drift characteristics of the first thermocouple and the second thermocouple to obtain the corrected temperature difference signal.

[0059] Specifically, during high-temperature measurements, the performance of thermocouples is easily affected by fluctuations in ambient temperature, a phenomenon known as "temperature drift." Temperature drift refers to the slow shift in the thermocouple's output signal due to changes in ambient temperature or long-term use, even when the measured temperature remains constant. To accurately eliminate this interference, this method requires generating real-time monitoring data of the ambient temperature around the contact interface. Based on pre-established temperature drift characteristic models or calibration data for the first and second thermocouples, the real-time ambient temperature data is used as input to accurately compensate for the previously obtained temperature difference signal. This ambient temperature ensures that the temperature difference signal fully reflects the true temperature deviation caused by heat flow, avoiding measurement errors introduced by environmental factors. The correction process can be expressed as:

[0060] ,

[0061] in, It is the corrected temperature difference signal. This is a preliminary temperature difference signal. It is the real-time ambient temperature. This represents a correction function established based on the temperature drift characteristics of thermocouples. This indicates the temperature drift characteristic parameter of the thermocouple.

[0062] For example, if it is known that the thermocouple may produce a temperature drift deviation of 0.5°C when the ambient temperature rises from 25°C to 50°C, then when the real-time ambient temperature reaches 50°C, the system will automatically calculate the corresponding compensation amount and remove it from the original temperature difference signal attenuation, thereby obtaining a more accurate temperature difference signal that has eliminated the influence of ambient temperature.

[0063] Based on the corrected temperature difference signal and the Seebeck coefficient of the thermocouple, the real-time heat flux density is initially calculated to obtain the preliminary heat flux density;

[0064] Specifically, after obtaining the temperature difference signal accurately corrected for ambient temperature, the real-time heat flux density at the contact interface is preliminarily determined using the inherent Seebeck coefficient of the thermocouple material, as well as the thermocouple's geometry and known thermal properties. The Seebeck coefficient is a key physical quantity for the thermocouple to convert temperature gradients into electromotive force, directly reflecting its thermoelectric conversion capability under temperature difference signals. The preliminary heat flux density calculation is typically based on a simplified thermal calculation model, approximating a one-dimensional steady-state assumption. This model correlates the modified temperature difference signal with the thermocouple's effective thermal resistance, thus providing a relatively fast and effective initial estimate of the heat flux. The following simplified model can be used for estimation:

[0065] ,

[0066] in, This represents the preliminary calculated real-time heat flux density; The inductance coefficient representing the thermocouple and the contact area; It is a temperature difference signal that has been corrected for ambient temperature; This indicates the corresponding thermoelectric distance between the two thermocouples.

[0067] For example, when the modified temperature difference signal is 5°C, the system will immediately calculate a preliminary heat flux density value, such as 200W / m², using the above formula based on the pre-set thermocouple equivalence coefficient and the equivalence coefficient of the dual thermocouples. This value will be used as the subsequent fine dynamic correction input.

[0068] Based on the initial heat flux density and considering the thermal response hysteresis characteristics of the thermocouple under high temperature conditions, a dynamic correction factor is introduced to correct the final real-time heat flux density at the contact interface.

[0069] Specifically, actual measurements of thermocouples in high-temperature environments often exhibit thermal response hysteresis, meaning their output signal cannot instantly reflect rapid changes in the measured surface temperature, thus affecting the accuracy of the initial heat flux density. To compensate for this dynamic behavior, this method introduces a dynamic correction factor. This factor can evaluate the transient response characteristics of the thermocouple in real time and adaptively adjust the predicted heat flux density based on the current rate of temperature change and thermal inertia. The introduction of the dynamic correction factor allows the calculated heat flux density to more accurately reflect the actual instantaneous heat transfer at the contact interface, significantly improving the real-time performance and accuracy of the measurement. This process can be described as follows:

[0070] ,

[0071] in, It is the final real-time heat flux density; This is the initial heat flux density; It is a dynamic modification factor that is adjusted in real time based on the dynamic characteristics of the thermocouple.

[0072] For example, when the temperature of the measured high-temperature surface changes rapidly, the initially calculated heat flux density may become outdated due to the continuous lag in heat flux density calculation. In this case, the dynamic correction factor will automatically adjust its value according to the rate and direction of temperature change, such as increasing it from 1.0 to 1.2, multiplying it by the expected heat flux density, so that the final output heat flux density can more quickly and accurately reflect the actual thermal state of the high-temperature surface, and remain optimal even under changing operating conditions.

[0073] Optionally, establishing the dynamic coupling model between the real-time heat flux density and the real-time temperature difference includes:

[0074] The high-temperature surface temperature measurement range is divided into several preset temperature intervals;

[0075] Specifically, within the high-temperature surface temperature measurement range, the thermophysical properties of materials (such as thermal conductivity and specific heat capacity) and the response characteristics of thermocouples are often nonlinear, and this nonlinearity varies significantly with temperature. To more accurately describe the complex dynamic relationship between real-time heat flux density and real-time temperature difference, it is necessary to divide the entire broad correlated temperature measurement range into several pre-defined temperature intervals with specific boundaries. The thermophysical properties and thermocouple responses within these intervals can be more effectively estimated or modeled, thus laying the foundation for establishing more accurate sub-models. The division of these intervals can be based on experimental data, theoretical analysis, or considerations of accuracy requirements in practical applications.

[0076] For example, if the measurement range of the high-temperature surface temperature is 200°C to 1000°C, the range can be divided into several sub-intervals, such as: the first interval is 200°C to 400°C, the second interval is 400°C to 700°C, and the third interval is 700°C to 1000°C. This division makes the nonlinear characteristics of the thermocouple and the material properties more stable within each narrower temperature range, which is beneficial for establishing gradient models.

[0077] For each temperature range, a nonlinear dynamic coupling sub-model including contact thermal resistance parameters is established, wherein the nonlinear dynamic coupling sub-model characterizes the nonlinear relationship between heat flux density and temperature difference within that temperature range.

[0078] Specifically, for each defined temperature range, a dynamic correlation sub-model needs to be established to accurately characterize the nonlinear relationship between real-time heat flux density and real-time temperature difference within that range. These sub-models must not only reflect the instantaneous response between temperature and heat flux but also significantly include contact thermal resistance parameters for subsequent online analysis. Due to the complexity of heat transfer processes at high temperatures, constructing these range-specific models allows for a more refined capture of the dynamic characteristics of contact thermal resistance in different temperature ranges. For example, a simplified nonlinear correlation sub-model can be represented as:

[0079] ,

[0080] in, This represents the real-time heat flux density at the current moment; This indicates the real-time temperature difference at the current moment; It represents the rate of change of real-time temperature difference over time, reflecting dynamic characteristics; This indicates the contact thermal resistance parameters included within this temperature range; These are model coefficients, which are nonlinear or dynamic characteristic parameters related to this temperature range.

[0081] For example, within the temperature range of 200°C to 400°C, the contact thermal resistance may exhibit a specific type of non-changing trend due to changes in the material's oxide layer or contact state. Therefore, a dynamic correlation sub-model based on local regression can be established for this range, with its factors specifically trained and optimized for this temperature range, thereby maximizing the accuracy of heat flux density calculation and contact thermal resistance within this temperature range.

[0082] Based on the current real-time temperature value, the nonlinear dynamic coupling sub-model of the corresponding temperature range is automatically selected and applied to obtain the dynamic coupling relationship model between real-time heat flux density and real-time temperature difference.

[0083] Specifically, during actual operation, the system needs to intelligently determine the preset temperature range corresponding to the currently measured temperature value. Once the current temperature range is determined, the system will automatically select and activate the corresponding nonlinear dynamic correlation sub-model. This dynamic selection and application mechanism ensures that the most suitable and optimized model for the current thermophysical state is always used to characterize the dynamic correlation between real-time heat flux density and real-time temperature difference throughout the entire high-temperature measurement range. This structural modeling and automatic switching strategy greatly improves the applicability and accuracy of the model across the entire operating range.

[0084] For example, suppose the current real-time measured surface temperature is 450°C. The system will first identify the preset temperature range of "400°C to 700°C" within 450°C. Furthermore, the system will automatically call and apply a locally dynamically connected sub-model specifically built for this range to process the current real-time heat flux density and temperature difference data. When the temperature further increases and approaches the 700°C limit, the system will smoothly switch to the sub-model corresponding to the "700°C to 1000°C" range, thereby ensuring the continuity and accuracy of the measurement and error process.

[0085] Optionally, the online identification of the contact thermal resistance parameters in the dynamic coupling model using the recursive least squares algorithm includes:

[0086] Based on the measured data of real-time heat flux density and real-time temperature difference, the parameter set of the recursive least squares algorithm is initialized.

[0087] Specifically, before using the recursive least squares (RLS) algorithm to perform online analysis of the contact thermal resistance parameters in the dynamic correlation model, it is necessary to first set the initial parameter set of the algorithm in real time. This includes initializing the parameter estimation vector. and the initial covariance matrix Sparsity in parameter estimation typically involves the contact thermal resistance parameters to be determined and their associated model coefficients, while the initial covariance matrix reflects the uncertainty of the parameter estimates. Reasonable initialization is crucial for the convergence speed and stability of the algorithm; it is usually set based on prior knowledge or empirical values ​​of the system behavior.

[0088] For example, when the algorithm starts, the initial parameter estimates can be set to small values ​​close to zero, such as 0.01 for all elements. Simultaneously, the initial covariance matrix... Set it to a larger diagonal matrix, such as having 1000 for each diagonal element and the mean for the off-diagonal elements. This setup implies that the parameter estimates have corresponding uncertainty at the beginning of the algorithm, thus giving the algorithm more "learning" space and enabling it to quickly adapt to real-world systems.

[0089] The residual between the predicted output of the dynamic coupling relationship model and the actual measured value is calculated in real time, and the forgetting factor in the recursive least squares algorithm is adaptively adjusted according to the magnitude of the residual.

[0090] Specifically, at each sampling moment, the residual (prediction error) between the predicted output of the dynamic coupling model and the actual measured value is calculated in real time. This residual is a key indicator of the predictive accuracy of the adaptive model. Based on the magnitude of this residual, the gradient factor (forgetting factor) λ is adaptively adjusted. The forgetting factor controls the degree to which the algorithm "forgets" historical data: a large forgetting factor has a significant impact on historical data and updates slowly, making it suitable for dynamically changing systems; a small forgetting factor means it pays more attention to new data and updates rapidly, making it suitable for dynamically changing systems. Through the forgetting adjustment factor, the algorithm can achieve a balance between tracking the dynamic changes of the system and blocking measurement noise. The calculation method is usually as follows:

[0091] ,

[0092] in, This represents the prediction residual at the current moment; This represents the actual measured output at the current moment (e.g., real-time heat flux density). This indicates the regression support at the current moment, including the model's input data (e.g., real-time temperature difference and related terms). Mining parameters for the previous time step.

[0093] For example, if there is a large difference between the actual heat flux density and the model prediction, i.e., the absolute value of the residual increases, it indicates that the model needs to adapt to the new system state more quickly. In this case, the algorithm increases the value of the forgetting factor λ (setting it closer to 0), thereby giving more weight to the new data and accelerating parameter updates. Conversely, if the residual is small, the value of λ is increased (setting it closer to 1) to improve stability and enhance noise resistance.

[0094] The contact thermal resistance parameters are iteratively updated to the preset convergence condition or to achieve stable identification accuracy using an adjusted forgetting factor and a recursive least squares algorithm, thereby obtaining dynamic thermal resistance compensation values.

[0095] Specifically, an adjusted forgetting factor is used, and a recursive least squares algorithm is employed to iteratively update the contact thermal resistance parameters in the dynamic correlation model. At each sampling time, the algorithm utilizes the new residual information and the adjusted covariance matrix to update the parameter estimates according to the recursive formula. This iterative process continues until the contact thermal resistance parameters output by the iterations reach the initial convergence condition (e.g., the parameter change is less than a threshold) or a stable accuracy. Finally, the contact thermal resistance parameter values ​​obtained from each iteration, i.e., the real-time dynamic thermal resistance compensation values, are output for subsequent temperature signal correction. The core parameter and gain matrix update formulas are as follows:

[0096] ,

[0097] ,

[0098] ,

[0099] in, Represents the gain matrix; Represents the initial covariance matrix; Forgetting factor; It is the identity matrix; This is to compensate for the estimated parameters at the current moment.

[0100] like Figure 3 As shown, the convergence process of the recursive least squares algorithm in real-time tactile contact thermal resistance parameters is illustrated by the corner curve, and how the forgetting factor is adaptively adjusted according to the magnitude of the tactile residual.

[0101] For example, as the system continues to run and the contact state between the thermocouple and the high-temperature surface remains relatively stable, the contact thermal resistance parameter obtained by the recursive least squares algorithm will gradually tend towards a stable value. Even with a small amount of random noise, the parameter will arrive within a very small range, indicating that the humidity accuracy has reached stability. At this point, the system will continuously output this steady-state dynamic thermal resistance compensation value to accurately correct the high-temperature surface temperature measurement results.

[0102] Optionally, using the dynamic thermal resistance compensation value to synchronously compensate and correct the first temperature signal and the second temperature signal includes:

[0103] Based on the distance and thermal contact characteristics between the first and second thermocouples and the high-temperature surface, the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals is determined.

[0104] Specifically, the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals needs to be accurately determined based on the physical distances of the first and second thermocouples to the high-temperature surface and their unique thermal contact characteristics. Since the two thermocouples may not be in complete contact or may have slightly different distances from the surface, their "potential" and resistance to the overall contact thermal resistance will differ. Therefore, to make the compensation more accurate, it is necessary to establish a local thermal resistance distribution model between the thermocouples and the high-temperature surface, or through experimental calibration, to determine the contribution of each thermocouple to the total thermal resistance, and thus determine their respective compensation weights. This allocation ensures that the compensation value can more realistically reflect the actual effect of thermal resistance on each thermocouple signal.

[0105] For example, if the measuring end of the first thermocouple is in closer contact with the high-temperature surface, or its embedment depth is closer to the surface, while the contact of the second thermocouple is slightly looser, then the signal of the first thermocouple may be more directly and significantly affected by the contact thermal resistance. In this case, the dynamic thermal resistance compensation value assigned to the first temperature signal will have a relatively higher weight, in order to prioritize the correction of its signal and ensure more accurate compensation at more critical measurement points.

[0106] The dynamic thermal resistance compensation value is applied to the first temperature signal and the second temperature signal according to the assigned weight to compensate and correct them, so as to obtain the first temperature signal and the second temperature signal after compensation and correction.

[0107] Specifically, after obtaining the weighted allocation of the dynamic thermal resistance compensation values, the system compensation values ​​are monitored according to these weights for the first and second temperature signals, and synchronous compensation corrections are performed. This means that the dynamic thermal resistance compensation values ​​are not simply distributed equally among the two signals, but rather, based on their respective sensitivity to contact thermal resistance, the weighted compensation values ​​are added to or corrected to the original temperature measurement signals. This effectively eliminates the independent influence of contact thermal resistance on each thermocouple signal, thus obtaining the finely corrected first and second temperature signals. This correction process can be expressed as:

[0108] ,

[0109] ,

[0110] in, and These represent the first and second temperature signals after compensation and correction, respectively; and These represent the original first and second temperature signals, respectively; and These represent the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals, respectively. ; This represents the dynamic thermal resistance compensation value.

[0111] For example, suppose the dynamic thermal resistance compensation value is determined to be +2.5°C, and according to the previous weighting assignment, the weight of the first temperature signal is 0.7 and the weight of the second temperature signal is 0.3. Then, the system will allocate 1.75°C out of 2.5°C. Check and modify the first temperature signal, and set it to 0.75°C. The second temperature signal is then evaluated and corrected. In this way, the original measurements of the two thermocouples can be synchronously and individually compensated according to the actual degree to which they are affected by thermal resistance, thereby producing a corrected temperature signal that is closer to the true value.

[0112] The temperature value that best matches the preset measurement target is selected and output from the compensated and corrected first temperature signal and the compensated and corrected second temperature signal as the high-temperature surface temperature measurement result, wherein the preset measurement target is the actual surface temperature or the temperature at a certain depth.

[0113] Specifically, after acquiring the first temperature signal and the second temperature signal after dynamic thermal resistance compensation correction, the system selects and outputs the temperature value that best matches the preset measurement target from these two corrected temperature signals as the final high-temperature surface temperature measurement result. The preset measurement target can be the actual surface temperature or the temperature at a specific depth inherent within the material. The system selects a strategy, adjusts data, and considers the application's requirements for specific temperature measurement points based on the actual installation location of the thermocouple. This intelligent selection ensures that the output temperature value is not only accurate but also meets the specific needs of the user or application scenario.

[0114] For example, if the preset measurement target is to obtain the true surface temperature at a high temperature, and it is known that the measuring end of the first thermocouple is more closely attached to the surface while the second thermocouple is slightly farther away from the surface, then the system will choose to output and compensate for the modified first temperature signal as the final surface measurement temperature. Conversely, if the measurement target is the temperature at a specific depth, and the second thermocouple is located at that depth, then even if the first thermocouple is closer to the surface, the system can choose to output the modified second temperature signal to meet specific measurement needs.

[0115] Optionally, the correction by introducing a dynamic correction factor includes:

[0116] The operating temperatures of the first and second thermocouples and the ambient temperature are acquired in real time, and the transient response time constant of the thermocouples under the current operating conditions is dynamically calculated based on historical data.

[0117] Specifically, obtaining the operating temperatures of the first and second thermocouples, as well as the ambient temperature, is fundamental for dynamically calculating the transient response time of the thermocouples under current operating conditions. The transient response time of a thermocouple typically characterizes its response to temperature changes; it is not fixed but dynamically depends on factors such as operating temperature, ambient medium, thermocouple size, and structure. This method utilizes historical data (e.g., response records under similar operating conditions) and real-time temperature information, employing empirical models, regression analysis, or machine learning algorithms to dynamically provide an approximate instantaneous response time of the thermocouple under the current specific operating condition. This ensures an accurate understanding of the thermocouple's dynamic characteristics, providing corrective results for subsequent accurate estimation of dynamic parameters.

[0118] For example, when a thermocouple rapidly transitions from room temperature to a high-temperature environment, its response speed is affected by the convective heat transfer coefficient of the surrounding fluid. By analyzing historical experimental data, an empirical velocity relationship model can be established between the transient response time and the thermocouple's operating temperature, the fluid temperature, and the ambient temperature. For instance, as the operating temperature increases, the thermocouple's response time may be affected by enhanced radiative heat transfer, and the system will dynamically adjust this estimate based on real-time temperature data.

[0119] Based on the transient response time constant, the Kalman filter algorithm is used to iteratively estimate and update the dynamic correction factor;

[0120] Specifically, after obtaining the transient response time of the thermocouple under the current operating conditions, this method employs the Kalman simulation response algorithm. The Kalman filter is a highly efficient linear filter capable of estimating the state variables of a dynamic system from a series of noisy measurement data. The transient response time of the thermocouple, as one of the input parameters of the Kalman filter, influences its internal state and initial model. Based on the system dynamic model and real-time measurement data, a dynamic correction factor is predicted. Then, the predicted value is corrected according to the prediction residual (the difference between the actual measured value and the predicted value), thereby achieving optimal estimation and real-time updating of the dynamic correction factor. This allows the dynamic correction factor to smoothly and accurately track the actual dynamic response characteristics of the thermocouple, providing a reliable estimate even under environmental noise.

[0121] For example, suppose that under a certain high-temperature operating condition, the thermocouple's response lag leads to a continuous underestimation of the heat flux density. The Kalman filter continuously receives the phase (residual) between the actual heat flux value and the model's predicted value. Based on the dynamic characteristics indicated by the instantaneous response time of the thermocouple, it iteratively adjusts the internal state variables, thereby gradually increasing the dynamic correction factor and tending towards a value that accurately compensates for the lag effect. Even if random noise exists in the measurement, the Kalman filter can output a smoothed and corrected dynamic factor through its optimal estimation characteristics.

[0122] Based on the estimated and updated dynamic correction factor, combined with the preliminary heat flux density, the final real-time heat flux density at the contact interface is obtained.

[0123] Specifically, based on the Kalman filter simulation estimation, the system accurately estimates and updates the dynamic correction factor in real time. This correction factor is then combined with the initially calculated heat flux density to obtain the real-time heat flux density at the contact interface. The dynamic factor plays a crucial compensating role here, correcting for transient dynamics in the initial heat flux density caused by thermocouple thermal response hysteresis, changes in thermal contact, and other factors. This ensures that the output heat flux density not only accurately corrects for the actual situation but also accurately captures rapid changes under high-temperature conditions, significantly improving the accuracy and reliability of heat flux measurement under dynamic operating conditions.

[0124] For example, if the initial heat flux density calculation result is 150 W / m², and the dynamic correction factor obtained after Kalman filter estimation is 1.1, this means that the initial value is underestimated under the current operating conditions. The system presets the initial heat flux density multiplied by this dynamic correction factor, and the final output real-time heat flux density is 165 W / m². This modified value can more realistically reflect the actual instantaneous heat flux state of the high-temperature surface, providing reliable data even when the temperature changes rapidly.

[0125] Optionally, the adaptive adjustment of the forgetting factor in the recursive least squares algorithm based on the magnitude of the residual includes:

[0126] Based on the historical operating data of the dynamic coupling relationship model, a preset threshold for the residual is determined, including an upper threshold and a lower threshold.

[0127] Specifically, determining the preset thresholds for the residuals based on historical operational data from the dynamic correlation model is a crucial step in achieving adaptive adjustment of the forgetting factor. These thresholds, including upper and lower limits, define the sensitive range of the model's predictions. Their determination is typically achieved through statistical analysis of long-term residual data under normal system operation, such as calculating the residual deviation standard and mean, combined with engineering experience and requirements for sorting performance. These thresholds serve as the basis for judging the magnitude of the residuals, guiding the adjustment strategy of the forgetting factor to balance the algorithm's response speed and stability.

[0128] For example, by analyzing historical operating data of the model over the past few weeks or months, if it is found that 95% of the absolute values ​​of the residuals are concentrated between 0.01 and 0.5, the lower limit threshold for the residuals can be set to 0.01, and the upper limit threshold to 0.5. These thresholds clearly indicate the range between normal motion and abnormal measurements, providing a reliable reference for subsequent adjustment of gradient errors.

[0129] When the absolute value of the residual exceeds the upper limit threshold, the value of the forgetting factor is reduced to improve the response speed of the recursive least squares algorithm to new data.

[0130] Specifically, when the absolute value of the residual between the model's predicted output and the actual measured value exceeds a preset upper threshold, this usually indicates a significant change in the system, such as a transient effect of obvious local thermocouple loss or enlargement in the high-temperature surface contact state. Reducing the forgetting factor (λ) decreases the weight of historical data in data updates, thereby improving the algorithm's response speed to new measurement data and enabling the contact thermal resistance correction parameters to adapt to new thermal contact conditions more quickly.

[0131] For example, suppose that at a certain moment, due to the sudden deformation of the measured material, the thermocouple contact state changes, causing the absolute value of the residual to rapidly increase from 0.3 to 1.2, exceeding the set limit threshold of 0.5. At this time, the value of the forgetting factor λ will be automatically reduced from 0.99 to 0.90. This makes the algorithm pay more attention to the current large residual information in the following iterations, thereby quickly correcting the contact thermal resistance parameters to adapt to the new thermal contact conditions.

[0132] When the absolute value of the residual is lower than the lower threshold, the value of the forgetting factor is increased to improve the convergence stability and noise resistance of the recursive least squares algorithm.

[0133] Specifically, when the absolute value of the residual between the model's predicted output and the actual measured value is lower than a preset lower threshold, it indicates that the system is in a relatively stable state and the model is relevant to the accuracy of the current thermocouple contact thermal resistance. In this case, to improve the stability and noise resistance of the least squares algorithm, an asymptotic factor λ needs to be added. Increasing the residual factor will increase the weight of historical data in parameter updates, making the algorithm's update process smoother and less susceptible to random noise, thus providing more robust and refined contact thermal resistance results when the system is stable.

[0134] For example, if the system has been running for a period of time and the absolute value of the difference is stable at 0.005, which is far below the lower limit threshold of 0.01, this indicates that it is already very stable and accurate. The value of λ will automatically increase from 0.99 to 0.999, making the algorithm more reliant on historical data in subsequent updates, thereby effectively resisting the small disturbances of sporadic noise measurements on the oscillation results and maintaining the stability and smoothness of parameter estimation.

[0135] When the absolute value of the residual is between the lower threshold and the upper threshold, the value of the forgetting factor remains unchanged to maintain the balance of recognition.

[0136] Specifically, when the absolute value of the model's predicted residuals falls between the lower and upper bounds of the outer threshold, this indicates that the system is within a normal fluctuation range, the current signal recovery accuracy of the model's actions is precise, and no significant systemic changes requiring adjustment of the allergen factor have occurred. In this case, maintaining the value of the forgetting factor unchanged is the optimal strategy, without the need for additional adjustments. This maintains the balance between response speed and noise robustness in the push least squares algorithm, avoiding missing factor actions and thus ensuring the stability and efficiency of the algorithm in recent runs.

[0137] For example, if the absolute value of the current residual is 0.2, and the current lower threshold is between 0.01 and the upper threshold is between 0.5, then the system will maintain the forgetting factor λ. This means that the algorithm will continue to process the data at the existing “forgetting rate”, neither becoming sensitive to noise due to excessive pursuit of response speed, nor losing the ability to track serious changes due to excessive smoothing.

[0138] Optionally, the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals includes:

[0139] Based on the position and geometry of the first and second thermocouples, as well as the material properties of the high-temperature surface, a local thermal resistance distribution model between the thermocouples and the high-temperature surface is established.

[0140] Specifically, to accurately determine the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals, a local thermal resistance distribution model between the thermocouples and the high-temperature surface needs to be established based on the precise positions and geometry of the first and second thermocouples, as well as the material thermal properties of the high-temperature surface. The model considers the influence of factors such as the equivalent morphology of the interface between the thermocouple and the measured surface, the material thermal conductivity, and the contact pressure distribution on the local thermal resistance. By establishing this model, the actual thermal resistance path traversed from the high-temperature surface to the measuring end of each thermocouple can be determined.

[0141] like Figure 4 As shown in the figure, the graph uses multiple curves to illustrate the trend of local contact thermal resistance as a function of operating temperature under different assumed contact conditions.

[0142] For example, suppose the measuring end of the first thermocouple is in close contact with the hot surface through a thin bonding layer, while the measuring end of the second thermocouple is in contact with the surface through a slightly larger air gap. Based on these different contact and geometric medium properties, their respective local thermal resistance models can be established through finite element analysis or simplified thermal circuit models, resulting in differences in their thermal resistance distributions.

[0143] Based on the local thermal resistance distribution model and combined with the real-time heat flux density, the thermally affected areas of the first thermocouple and the second thermocouple and their contribution to the overall contact thermal resistance are dynamically calculated.

[0144] Specifically, after establishing a local thermal resistance distribution model between the thermocouple and the high-temperature surface, this method combines real-time heat flux density data to dynamically calculate the heat-affected zones (TAZs) of the thermocouple and the second thermocouple on the high-temperature surface, as well as their contributions to the overall contact thermal resistance. The TAZ refers to the area surrounding the thermocouple's thermal contact characteristics that influences the temperature field and heat flux distribution. By analyzing how heat flux passes through various local contact thermal resistance paths and combining the magnitude and direction of real-time heat flux density, the contact thermal resistance share "borne" by each thermocouple can be determined, i.e., its contribution ratio to the overall contact thermal resistance. This dynamic calculation ensures accurate assessment of the relative importance of each thermocouple in thermal resistance elimination, even when thermal conditions change.

[0145] For example, when the total heat flux density of the high-temperature surface changes, if the local thermal resistance distribution model shows that the heat-affected zone of the thermocouple is larger, or the heat flux density at its intermittent locations is higher, then the system will dynamically calculate that the contribution of that thermocouple to the overall contact thermal resistance increases accordingly. For instance, at a specific moment, the contribution of the heat-affected zone and local heat flux of the first thermocouple to the total thermal resistance reaches 70%, while the contribution of the second thermocouple is 30%.

[0146] Based on the contribution, the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals is determined.

[0147] Specifically, after accurately and dynamically calculating the contributions of the first and second thermocouples to the overall contact thermal resistance, the precise weighting of the dynamic thermal resistance compensation value on the first and second temperature signals can be determined based on these contributions. This allocation strategy is careful and reasonable: the estimated contribution of the thermocouples is more affected by the contact thermal resistance, therefore a higher proportion of the dynamic thermal resistance value should be allocated for correction. By weighting the compensation values ​​according to the actual contribution ratio of each thermocouple, the precision and effectiveness of the compensation can be guaranteed, ensuring that the measurement signal of each thermocouple receives a correction that best reflects its actual degree of thermal resistance influence.

[0148] For example, if, according to dynamic calculations, the first thermocouple contributes 70% to the overall contact thermal resistance, the second thermocouple contributes 70% to the overall contact thermal resistance, and the third thermocouple contributes 30% to the overall contact thermal resistance, then when the modified temperature measurement result is closer to the true temperature of the surface, 70% of the compensation value will be allocated to the first temperature signal for correction, while the remaining 30% will be allocated to the second temperature signal.

[0149] Based on the same inventive concept, such as Figure 5 As shown, the present invention also provides a high-temperature surface contact thermal resistance elimination system based on dual thermocouple compensation, the system comprising:

[0150] The temperature acquisition module is used to synchronously acquire the real-time temperature values ​​of the first thermocouple and the second thermocouple to obtain the first temperature signal and the second temperature signal.

[0151] The temperature difference calculation module is used to calculate the real-time temperature difference between the first temperature signal and the second temperature signal to obtain the temperature difference signal;

[0152] The heat flux density calculation module is used to calculate the real-time heat flux density at the contact interface based on the temperature difference signal and the Seebeck coefficient of the thermocouple.

[0153] The dynamic coupling model establishment module is used to establish a dynamic coupling relationship model between the real-time heat flux density and the real-time temperature difference, wherein the model includes contact thermal resistance parameters;

[0154] The online thermal resistance parameter identification module is used to identify the contact thermal resistance parameters in the dynamic coupling relationship model online using the recursive least squares algorithm, and generate dynamic thermal resistance compensation values.

[0155] The temperature compensation correction and output module is used to use the dynamic thermal resistance compensation value to synchronously compensate and correct the first temperature signal and the second temperature signal, and output the high-temperature surface temperature measurement result after compensation and correction.

[0156] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0157] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation, characterized in that, The method includes: The real-time temperature values ​​of the first thermocouple and the second thermocouple are collected simultaneously to obtain the first temperature signal and the second temperature signal. Calculate the real-time temperature difference between the first temperature signal and the second temperature signal to obtain the temperature difference signal; The real-time heat flux density at the contact interface is calculated based on the temperature difference signal and the Seebeck coefficient of the thermocouple. A dynamic coupling model is established between the real-time heat flux density and the real-time temperature difference, wherein the dynamic coupling model includes contact thermal resistance parameters; The contact thermal resistance parameters in the dynamic coupling model are identified online using a recursive least squares algorithm to generate dynamic thermal resistance compensation values. This online identification of the contact thermal resistance parameters in the dynamic coupling model includes: initializing the parameter set of the recursive least squares algorithm based on the measured data of real-time heat flux density and real-time temperature difference; calculating the residual between the predicted output of the dynamic coupling model and the actual measured value in real time, and adaptively adjusting the forgetting factor in the recursive least squares algorithm according to the magnitude of the residual; iteratively updating the contact thermal resistance parameters to a preset convergence condition or achieving stable identification accuracy using the adjusted forgetting factor and the recursive least squares algorithm to obtain dynamic thermal resistance compensation values. The dynamic thermal resistance compensation value is used to synchronously compensate and correct the first temperature signal and the second temperature signal, and the high-temperature surface temperature measurement result after compensation and correction is output. The adaptive adjustment of the forgetting factor in the recursive least squares algorithm based on the magnitude of the residuals includes: determining a preset threshold for the residuals based on historical operating data of the dynamic coupling relationship model, including an upper threshold and a lower threshold; when the absolute value of the residuals exceeds the upper threshold, decreasing the value of the forgetting factor to improve the response speed of the recursive least squares algorithm to new data; when the absolute value of the residuals is lower than the lower threshold, increasing the value of the forgetting factor to improve the convergence stability and noise resistance of the recursive least squares algorithm; and when the absolute value of the residuals is between the lower and upper thresholds, maintaining the value of the forgetting factor to preserve the balance of identification.

2. The method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation according to claim 1, characterized in that, Calculating the real-time temperature difference between the first temperature signal and the second temperature signal includes: The first temperature signal and the second temperature signal are subjected to real-time digital filtering to eliminate random noise interference, resulting in filtered first temperature signal and filtered second temperature signal. Based on the respective temperature response characteristics of the first and second thermocouples, temperature-dependent calibration is performed on the filtered first temperature signal and the filtered second temperature signal. The temperature difference signal is obtained by calculating the real-time difference between the calibrated first temperature signal and the calibrated second temperature signal.

3. The method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation according to claim 1, characterized in that, The calculated real-time heat flux density at the contact interface includes: The real-time ambient temperature of the environment where the contact interface is located is obtained, and the temperature difference signal is corrected for ambient temperature based on the temperature drift characteristics of the first thermocouple and the second thermocouple to obtain the corrected temperature difference signal. Based on the corrected temperature difference signal and the Seebeck coefficient of the thermocouple, the real-time heat flux density is initially calculated to obtain the preliminary heat flux density; Based on the initial heat flux density and considering the thermal response hysteresis characteristics of the thermocouple under high temperature conditions, a dynamic correction factor is introduced to correct the final real-time heat flux density at the contact interface.

4. The method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation according to claim 1, characterized in that, Establishing the dynamic coupling model between the real-time heat flux density and the real-time temperature difference includes: The high-temperature surface temperature measurement range is divided into several preset temperature intervals; For each temperature range, a nonlinear dynamic coupling sub-model including contact thermal resistance parameters is established, wherein the nonlinear dynamic coupling sub-model characterizes the nonlinear relationship between heat flux density and temperature difference within that temperature range. Based on the current real-time temperature value, the nonlinear dynamic coupling sub-model of the corresponding temperature range is automatically selected and applied to obtain the dynamic coupling relationship model between real-time heat flux density and real-time temperature difference.

5. The method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation according to claim 1, characterized in that, Using the dynamic thermal resistance compensation value to synchronously compensate and correct the first temperature signal and the second temperature signal includes: Based on the distance and thermal contact characteristics between the first and second thermocouples and the high-temperature surface, the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals is determined. The dynamic thermal resistance compensation value is applied to the first temperature signal and the second temperature signal according to the assigned weight to compensate and correct them, so as to obtain the first temperature signal and the second temperature signal after compensation and correction. The temperature value that best matches the preset measurement target is selected and output from the compensated and corrected first temperature signal and the compensated and corrected second temperature signal as the high-temperature surface temperature measurement result, wherein the preset measurement target is the actual surface temperature or the temperature at a certain depth.

6. The method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation according to claim 3, characterized in that, The correction by introducing a dynamic correction factor includes: The operating temperatures of the first and second thermocouples and the ambient temperature are acquired in real time, and the transient response time constant of the thermocouples under the current operating conditions is dynamically calculated based on historical data. Based on the transient response time constant, the Kalman filter algorithm is used to iteratively estimate and update the dynamic correction factor; Based on the estimated and updated dynamic correction factor, combined with the preliminary heat flux density, the final real-time heat flux density at the contact interface is obtained.

7. The method for eliminating high-temperature surface contact thermal resistance based on dual thermocouple compensation according to claim 5, characterized in that, The weighting of the dynamic thermal resistance compensation value on the first and second temperature signals includes: Based on the position and geometry of the first and second thermocouples, as well as the material properties of the high-temperature surface, a local thermal resistance distribution model between the thermocouples and the high-temperature surface is established. Based on the local thermal resistance distribution model and combined with the real-time heat flux density, the thermally affected areas of the first thermocouple and the second thermocouple and their contribution to the overall contact thermal resistance are dynamically calculated. Based on the contribution, the weighting of the dynamic thermal resistance compensation value on the first and second temperature signals is determined.

8. A high-temperature surface contact thermal resistance elimination system based on dual thermocouple compensation, characterized in that, The system is applied to the high-temperature surface contact thermal resistance elimination method based on dual thermocouple compensation as described in any one of claims 1-7, and the system comprises: The temperature acquisition module is used to synchronously acquire the real-time temperature values ​​of the first thermocouple and the second thermocouple to obtain the first temperature signal and the second temperature signal. The temperature difference calculation module is used to calculate the real-time temperature difference between the first temperature signal and the second temperature signal to obtain the temperature difference signal; The heat flux density calculation module is used to calculate the real-time heat flux density at the contact interface based on the temperature difference signal and the Seebeck coefficient of the thermocouple. The dynamic coupling model establishment module is used to establish a dynamic coupling relationship model between the real-time heat flux density and the real-time temperature difference, wherein the model includes contact thermal resistance parameters; The online thermal resistance parameter identification module is used to identify the contact thermal resistance parameters in the dynamic coupling relationship model online using a recursive least squares algorithm, and generate dynamic thermal resistance compensation values. The online identification of the contact thermal resistance parameters in the dynamic coupling relationship model using the recursive least squares algorithm includes: initializing the parameter set of the recursive least squares algorithm based on the measured data of real-time heat flux density and real-time temperature difference; calculating the residual between the predicted output of the dynamic coupling relationship model and the actual measured value in real time, and adaptively adjusting the forgetting factor in the recursive least squares algorithm according to the magnitude of the residual; iteratively updating the contact thermal resistance parameters to a preset convergence condition or achieving stable identification accuracy using the adjusted forgetting factor and the recursive least squares algorithm, thereby obtaining dynamic thermal resistance compensation values. The temperature compensation correction and output module is used to use the dynamic thermal resistance compensation value to synchronously compensate and correct the first temperature signal and the second temperature signal, and output the high-temperature surface temperature measurement result after compensation and correction. The adaptive adjustment of the forgetting factor in the recursive least squares algorithm based on the magnitude of the residuals includes: determining a preset threshold for the residuals based on historical operating data of the dynamic coupling relationship model, including an upper threshold and a lower threshold; when the absolute value of the residuals exceeds the upper threshold, decreasing the value of the forgetting factor to improve the response speed of the recursive least squares algorithm to new data; when the absolute value of the residuals is lower than the lower threshold, increasing the value of the forgetting factor to improve the convergence stability and noise resistance of the recursive least squares algorithm; and when the absolute value of the residuals is between the lower and upper thresholds, maintaining the value of the forgetting factor to preserve the balance of identification.

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