Gas heat conductivity coefficient measurement data processing method, device, equipment and medium

Abstract

The invention relates to the technical field of gas heat conductivity coefficient measurement, and provides a gas heat conductivity coefficient measurement data processing method, device and equipment and a medium, and the method comprises the following steps: obtaining hot-wire temperature rise experiment data of hot-wire temperature rise along with time change measured by a transient hot-wire method experiment; the hot line temperature rise experiment data are corrected through a theoretical correction model, corrected theoretical temperature rise data are obtained, and the theoretical correction model is derived based on a physical model considering the hot line limited heat capacity and the radiation heat exchange effect; and determining the heat conductivity coefficient of the detected gas based on the corrected theoretical temperature rise data. According to the technical scheme, a test data processing correction method is provided, and the accuracy of a test result is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of gas thermal conductivity coefficient measurement, and particularly relates to a gas thermal conductivity coefficient measurement data processing method and device, equipment and a medium. BACKGROUND

[0002] The thermal conductivity coefficient is a key thermophysical property of a fluid, and as a core parameter for quantifying heat transfer, it has irreplaceable application value in engineering fields such as nuclear engineering, energy power, and chemical engineering involving heat transfer design. Especially in the accurate thermal design of the core of a nuclear reactor, the accuracy of the thermal conductivity coefficient measurement result of a gas working medium (such as helium) is directly related to the thermal safety of the system. If there is a deviation in the thermal conductivity coefficient data, it may lead to errors in heat transfer calculation, and thus cause serious engineering accidents. Therefore, the demand for high-precision measurement of the thermal conductivity coefficient of a gas is particularly urgent.

[0003] The transient hot wire method is one of the non-steady-state thermal conductivity measurement methods with the highest measurement precision and the widest application range recognized internationally. It has the advantages of short measurement time and adaptability to various media (covering solid, liquid, gas and powder materials), and has become the mainstream technology for thermal conductivity measurement. The core theoretical basis of this method is a one-dimensional radial non-steady-state heat conduction ideal model in an infinite medium, and its core assumptions include: 1) the hot wire is simplified as a linear heat source with infinite axial length and infinitesimal diameter; 2) the influence of the physical properties of the hot wire itself, such as heat capacity and thermal conductivity, on the measurement is ignored; 3) the hot wire and the fluid to be measured are in thermal equilibrium before heating; 4) the interference of additional heat exchange forms such as convective heat transfer and radiation heat transfer during heating is ignored; 5) the heating power applied by the hot wire is constant; 6) the fluid to be measured has no boundary constraint in the axial and radial directions, forming an infinite heat transfer environment.

[0004] However, in actual measurement scenarios, the assumptions of the ideal model are difficult to fully satisfy, resulting in inevitable deviations between the actual measurement conditions and the theoretical model. Among them, some deviations (such as the hot wire end effect) can be alleviated by optimizing the structure design of the test device, but another type of core deviation, such as the temperature rise delay caused by the finite heat capacity of the hot wire itself, the radiation heat loss between the hot wire and the environment, etc., cannot be eliminated by device improvement and must rely on targeted test data correction methods for compensation.

[0005] In the prior art, the theoretical correction schemes for the transient hot wire method are mostly developed for liquid working media. Due to the significant differences in thermal physical parameters (such as thermal conductivity and thermal diffusivity) between gases (especially helium) and liquids, and the more prominent influence mechanism of radiation heat transfer and heat capacity effect in a gas environment, the existing correction methods for liquid working media cannot be directly adapted to the measurement scenarios of gas working media such as helium, resulting in large deviations in the measurement results of the thermal conductivity coefficient of the gas, which is difficult to meet the demand for high-precision data in the field of nuclear engineering. SUMMARY

[0006] Embodiments of the present application provide a gas thermal conductivity measurement data processing method, device, equipment and medium, aiming at solving the technical problems in the related art that the existing correction method of liquid working medium cannot be directly adapted to the measurement scene of gas working medium such as helium, resulting in large deviation of gas thermal conductivity measurement results, and it is difficult to meet the demand for high-precision data in the field of nuclear engineering and other fields.

[0007] In a first aspect, the embodiments of the present application provide a gas thermal conductivity measurement data processing method, which comprises: obtaining hot-wire temperature rise experimental data of hot-wire temperature rise changing with time measured by a transient hot-wire method experiment; correcting the hot-wire temperature rise experimental data by a theoretical correction model to obtain corrected theoretical temperature rise data, wherein the theoretical correction model is derived based on a physical model considering hot-wire finite heat capacity and radiation heat transfer effect; determining the thermal conductivity of the measured gas based on the corrected theoretical temperature rise data.

[0008] In one embodiment, optionally, the representation of the theoretical correction model comprises: ΔTid = ΔTexp + C1·f1(t) - C2·f2(t); wherein ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the hot-wire temperature rise experimental data, C1·f1(t) represents a compensation term corresponding to the hot-wire finite heat capacity effect, wherein C1 represents a heat capacity correction coefficient related to the heat capacity of the hot-wire, f1(t) represents a function related to the experimental temperature rise rate, C2·f2(t) represents a deduction term corresponding to the radiation heat transfer effect, C2 represents a radiation correction coefficient related to the radiation heat transfer, and f2(t) represents a function related to time.

[0009] In one embodiment, optionally, the theoretical correction model specifically comprises: ; wherein ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the hot-wire temperature rise experimental data, r0 represents the hot-wire radius, t represents time, ε p represents the emissivity of the hot-wire, σ represents the Stefan-Boltzmann constant, T(r0,0) represents the initial hot-wire surface temperature, ρ w represents the density of the hot-wire, c pw represents the specific heat capacity of the hot-wire at constant pressure, λ represents the thermal conductivity, and T∞ represents the environmental temperature in the initial equilibrium state.

[0010] In one embodiment, optionally, determining the thermal conductivity of the measured gas based on the corrected theoretical temperature rise data comprises: determine a change relation of the logarithm of the temperature rise with time based on the corrected theoretical temperature rise data; perform differential geometric feature analysis on the change relation to identify an ideal heat conduction time period conforming to a one-dimensional radial non-steady heat conduction model; calculate a slope value corresponding to the change relation in the ideal heat conduction time period; calculate a heat conduction coefficient of the measured gas according to the slope value and a heating power of the hot wire.

[0011] In one embodiment, the differential geometric feature analysis on the change relation to identify an ideal heat conduction time period conforming to a one-dimensional radial non-steady heat conduction model includes: calculate a first-order derivative curve of the logarithm of the corrected theoretical temperature rise data with time; perform recursive piecewise linear fitting on the first-order derivative curve using a plurality of data windows of different widths; calculate a second-order derivative of each linear segment in each data window; identify the longest continuous data segment with the minimum and approaching zero variance of the second-order derivative as the ideal heat conduction time period based on the variance of the second-order derivative as a judgment basis.

[0012] In one embodiment, the heat conduction coefficient of the measured gas is calculated using the following formula: λ=q / (4πk); where λ represents the heat conduction coefficient of the measured gas, q represents the heating power of the hot wire, and k represents the slope value.

[0013] In one embodiment, the measured gas includes helium, and the measurement temperature is higher than 500 degrees Celsius.

[0014] In a second aspect, the embodiments of the present application provide a gas heat conduction coefficient measurement data processing device, including: an acquisition module configured to acquire hot wire temperature rise experimental data of hot wire temperature rise change with time measured through a transient hot wire method experiment; a correction module configured to correct the hot wire temperature rise experimental data through a theoretical correction model to obtain corrected theoretical temperature rise data, wherein the theoretical correction model is derived based on a physical model considering hot wire finite heat capacity and radiation heat transfer effect; a determination module configured to determine a heat conduction coefficient of a measured gas based on the corrected theoretical temperature rise data.

[0015] In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the gas thermal conductivity measurement data processing method when executing the computer program.

[0016] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the gas thermal conductivity measurement data processing method when executed by a processor.

[0017] In the scheme implemented by the above gas thermal conductivity measurement data processing method, device, equipment and medium, the hot-wire temperature rise experimental data of the hot-wire temperature rise changing with time measured by the transient hot-wire method experiment is obtained; the hot-wire temperature rise experimental data is corrected by a theoretical correction model to obtain corrected theoretical temperature rise data, wherein the theoretical correction model is derived based on a physical model considering the hot-wire finite heat capacity and radiation heat transfer effect; and the thermal conductivity of the measured gas is determined based on the corrected theoretical temperature rise data. Through the above technical scheme of the present application, the experimental data of the hot-wire temperature rise changing with time measured by the transient hot-wire method experiment is obtained, the experimental data is accurately corrected by using the theoretical correction model derived based on the physical model considering the hot-wire finite heat capacity and radiation heat transfer effect, the core non-ideal factor deviations caused by the temperature rise delay of the hot-wire self-heat capacity ignored in the traditional transient hot-wire method ideal model and the low temperature rise caused by the radiation heat loss are effectively offset, the experimental data containing deviations is converted into the theoretical temperature rise data conforming to the ideal heat conduction model, and then the thermal conductivity of the measured gas is determined based on the corrected reliable data. Not only does it break through the limitation of the existing correction method which is multi-oriented for liquid working medium and cannot adapt to gas working medium (such as helium) measurement scene, but also significantly improves the accuracy and data reliability of gas thermal conductivity measurement, simplifies the data processing process, avoids subjective errors caused by human intervention, ensures the consistency and repeatability of the results in different measurement scenes, provides reliable data support for the field of nuclear engineering and other fields with strict requirements for gas thermal conductivity precision, and reduces the engineering risk caused by data deviation. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any inventive labor.

[0019] Figure 1 A schematic flowchart of a gas thermal conductivity measurement data processing method according to an embodiment of the present application is shown.

[0020] Figure 2 A schematic flow chart of step S103 in the gas thermal conductivity measurement data processing method according to an embodiment of the present application is shown.

[0021] Figure 3 A schematic flow chart of step S202 in the gas thermal conductivity measurement data processing method according to an embodiment of the present application is shown.

[0022] Figure 4 A schematic diagram of the relationship between the first derivative and the logarithm of time according to an embodiment of the present application is shown.

[0023] Figure 5 A schematic diagram of the relationship between the second derivative and the logarithm of time according to an embodiment of the present application is shown.

[0024] Figure 6 A block diagram of the gas thermal conductivity measurement data processing apparatus according to an embodiment of the present application is shown.

[0025] Figure 7 A block diagram of the computer device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] In order to better understand the technical solutions of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0027] It should be clear that the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0028] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0029] Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features in the embodiments can be combined with each other as long as they do not conflict.

[0030] It should be noted that the embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0031] The basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The software technologies of artificial intelligence mainly include computer vision technology, robot technology, biometric identification technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0032] Please refer to Figure 1 , Figure 1 A schematic flowchart of a gas thermal conductivity measurement data processing method according to an embodiment of the present application is shown.

[0033] As Figure 1 shown, the gas thermal conductivity measurement data processing method based on the hot-wire method includes: Step S101, obtaining hot-wire temperature rise experimental data of the change of the hot-wire temperature rise with time measured by the transient hot-wire method experiment; The transient hot-wire method is a non-steady-state thermal conductivity measurement technology. A constant power is applied to heat a hot-wire placed in the measured medium, the change of the hot-wire temperature rise with time is recorded, the medium thermal conductivity is derived based on heat transfer theory, and the method has the characteristics of fast measurement speed, high precision, and wide adaptability.

[0034] The hot-wire temperature rise experimental data refers to a set of original data collected by a temperature sensor in real time during the experiment, which is the change of the temperature rise value (relative to the initial equilibrium temperature) of the hot-wire in the heating process with the heating time.

[0035] In this step, the hot-wire is placed in the measured gas environment to ensure that the hot-wire and the gas reach thermal equilibrium; the heating system is started to make the hot-wire heat at a constant power; the temperature of the hot-wire is continuously collected by a high-precision temperature sensor (such as a thermocouple or a platinum resistance); the temperature rise at different time points is calculated, wherein the temperature rise = real-time temperature - initial equilibrium temperature; and finally the hot-wire temperature rise experimental data covering the whole heating process is formed, and the data collection frequency needs to meet the accuracy requirements of subsequent correction and analysis.

[0036] In this way, through the characteristics of the transient hot-wire method, it is ensured that the data can capture the dynamic changes of the heat transfer process, avoiding the system errors easily occurring in steady-state measurement, and the continuity and high-frequency collection of the original data provide data support for subsequent accurate identification of the ideal heat conduction period.

[0037] Step S102, correcting the hot-wire temperature rise experimental data by a theoretical correction model to obtain corrected theoretical temperature rise data, wherein the theoretical correction model is derived based on a physical model considering the finite heat capacity of the hot-wire and the radiation heat transfer effect; The theoretical correction model is a mathematical model built on the principles of heat transfer to counteract the interference of non-ideal factors on experimental data. Its core function is to transform experimental data with bias into theoretical data that conforms to the ideal heat conduction model.

[0038] The limited heat capacity of a hot wire is its own ability to store heat (determined by the density, specific heat capacity, and volume of the hot wire material), which differs from the assumption of "zero heat capacity" in the ideal model, and will cause a delay in the temperature rise of the hot wire.

[0039] Radiation heat transfer is the phenomenon in which the surface of a hot wire transfers heat to the far-field environment through thermal radiation during the heating process, which causes the actual temperature rise of the hot wire to be lower than the temperature rise under ideal radiation-free conditions.

[0040] The corrected theoretical temperature rise data is the temperature rise data of the hot wire that conforms to the ideal model of "one-dimensional radial unsteady heat conduction in an infinite medium" after the deviation of non-ideal factors such as the finite heat capacity of the hot wire and radiation heat transfer has been offset by the theoretical correction model.

[0041] In one embodiment, optionally, the representation of the theoretical correction model includes: ΔTid =ΔTexp + C1·f1(t) - C2·f2(t); Wherein, ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the experimental temperature rise data of the hot wire, C1·f1(t) represents the compensation term corresponding to the finite heat capacity effect of the hot wire, where C1 represents the heat capacity correction coefficient related to the heat capacity of the hot wire, f1(t) represents the function related to the experimental temperature rise rate, C2·f2(t) represents the deduction term corresponding to the radiative heat transfer effect, C2 represents the radiation correction coefficient related to radiative heat transfer, and f2(t) represents the function related to time.

[0042] Among them, the compensation item (C1) f1(t): A mathematical term used to compensate for the temperature rise delay caused by the limited heat capacity of the hot wire. Its value changes with time and is positively correlated with the heat capacity characteristics of the hot wire.

[0043] Heat capacity correction factor (C1): A constant determined by the physical parameters of the heat wire itself. Its core components include the density of the heat wire, specific heat capacity at constant pressure, radius, etc., reflecting the degree of influence of the heat capacity of the heat wire on the temperature rise.

[0044] Deductions (C2) f2(t): A mathematical term used to offset the temperature rise loss caused by radiative heat transfer. Its value changes with time and is positively correlated with the intensity of radiative heat transfer.

[0045] Radiation correction factor (C2): A constant determined by the surface characteristics of the hot wire and the ambient temperature. Its core components include the emissivity of the hot wire surface, the Stefan-Boltzmann constant, and the absolute ambient temperature, reflecting the degree of influence of radiative heat transfer on temperature rise.

[0046] In this embodiment, the specific mathematical form of the theoretical correction model is clarified, making the correction process operable and repeatable, and avoiding vague expressions of the correction model; the separate design of compensation terms and deduction terms can accurately offset the influence of two types of non-ideal factors, making the correction logic clearer; the physical meaning of the correction coefficients and functions is clear, and they can be flexibly adjusted according to different hot wire parameters and experimental environments, enabling the model to adapt to various measurement scenarios and improving the versatility and adaptability of the method.

[0047] In one embodiment, the optional theoretical correction model specifically includes: ; Where ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the experimental temperature rise data of the hot wire, r0 represents the radius of the hot wire, t represents time, and ε p Let ρ represent the emissivity of the hot wire, σ represent the Stefan-Boltzmann constant, T(r0,0) represent the initial surface temperature of the hot wire, and ρ represent the emissivity of the hot wire. w c represents thermal density. pw λ represents the specific heat capacity of the hot wire at constant pressure, λ represents the thermal conductivity, and T∞ represents the ambient temperature at the initial equilibrium state.

[0048] Hot wire radius: The geometric parameter of the hot wire (unit: m), which is a key parameter for calculating the heat capacity and radiative heat transfer area of ​​the hot wire.

[0049] Hot wire emissivity: A parameter characterizing the radiation capability of the hot wire surface (value ranges from 0 to 1). The higher the emissivity, the stronger the heat transfer through radiation.

[0050] Stefan-Boltzmann constant: thermodynamic constant (value 5.67 × 10⁻⁶) -8 W / (m² K 4 ), is the core constant for calculating radiative heat transfer.

[0051] Heat wire density: The density of the heat wire material (unit: kg / m³) is one of the key parameters that determines the heat capacity of the heat wire.

[0052] Specific heat capacity of hot wire under constant pressure: The amount of heat required for a unit mass of hot wire material to increase its temperature by a unit under constant pressure (unit: J / (kg)). K) is one of the key parameters that determine the heat capacity of the hot wire.

[0053] In this embodiment, the quantization design of the model parameters makes the correction process more accurate and operable, avoiding the correction error caused by parameter ambiguity; all parameters are based on physical measurements or known constants, ensuring the objectivity and reliability of the model. Compared with the empirical formula-based correction method, the measurement accuracy is significantly improved; this model specifically compensates for two core deviations, namely the hot wire heat capacity and radiative heat transfer, and is particularly suitable for scenarios where non-ideal factors such as high temperature and gas have a significant impact, further expanding the scope of application of the method and providing more specific technical support for high-precision measurement.

[0054] Among them, the above theoretical correction model is obtained through the following scheme: (1)Establishment of the physical model We consider a more realistic physical model: The hot wire is an infinitely long cylinder with a radius of r0 and has finite thermophysical properties (density ρ w , specific heat capacity c pw , thermal conductivity λw).

[0055] The hot wire is surrounded by an infinite working medium of helium to be measured (thermal conductivity λ, thermal diffusivity α).

[0056] At the initial equilibrium temperature T0, the hot wire starts to be heated with a constant power q (per unit length).

[0057] Consider the radiative heat transfer between the hot wire surface and the far-field environment (temperature T∞).

[0058] (2)Control equations and solutions Under this model, the energy transfer of the system is controlled by the following equations: The heat conduction equation in the hot wire region (0 < r < r0): ; Among them, αw = λw / ( ρwcpw ) is the thermal diffusivity of the hot wire.

[0059] ② The heat conduction equation in the fluid region (r > r0), and the radiation term is introduced: ; Boundary condition 1 (at r = r0): Heat flux continuity, considering the heat derived from within the hot wire to be balanced with the heat introduced into the fluid and the surface radiative heat loss: ; Boundary condition 2 (at r = r0): Temperature continuity: Tw ( r 0, t ) = Tf (r 0, t ); Boundary condition 3 (at r → ∞): Tf (∞, t ) = T ∞; ; Initial condition (when t = 0): Tw ( r , 0) = Tf ( r , 0) = T 0; (3) Linearization and analytical solution The above system of equations is a strongly non - linear system, and the main non - linear term comes from the radiation term (T4 T∞ 4 ). To solve it, the present invention adopts the small temperature difference assumption and the linearization method, which are standard and effective means in this field for dealing with such problems. Let the temperature rise θ(r, t) = T(r, t) T0, and assume that the temperature rise during the measurement process is much smaller than the absolute temperature (i.e., θ << T0). Then the radiation term can be linearized as: Tf ( r 0, t )4 T ∞ 4 ≈ 4 T 0 3 θf ( r 0, t ) ; where it is assumed that T ∞ ≈ T 0. Substituting the linearized radiation term into the boundary conditions, the entire control system becomes linear. Subsequently, the Laplace transform technique is applied to solve this linear system. By performing the Laplace transform on the fluid region equation and solving the solution in the form of Bessel functions, combined with the solution of the hot - wire region and the boundary conditions, the expression of the hot - wire surface temperature rise in the Laplace space can be obtained .

[0060] Key inverse transform and asymptotic expansion Performing the inverse Laplace transform on is complex. The present invention cleverly uses the asymptotic expansion method of the inverse Laplace transform to solve the time - domain solution for medium time (i.e., the ideal heat conduction section where the heat capacity effect of the hot - wire itself has decayed and the convection effect has not yet significantly come into play). Through rigorous mathematical derivation, the experimentally measured temperature rise Δ Texp = θ ( r0, t The expression for ) is: ; Where γ is the Euler constant.

[0061] (5) The above formula clearly shows that the experimental temperature rise is similar to the ideal model temperature rise. The deviation mainly comes from two time-related terms: ( ρwcpwr 0 2 ) / (2 λ ) (1 / t ) Item: Represents the heat capacity effect of the hot wire, the effect of which decreases over time.

[0062] +(2 pσT 0 3 r 0) / λ t The term represents the radiative heat loss effect, which increases linearly with time.

[0063] Since the data processing focuses on the relationship between ΔT and lnt, the above expression is analyzed with respect to lnt, taking into account the temperature rise rate. After simplification and transformation, this invention finally obtains a formula for calculating the relationship between experimental values ​​ΔT and lnt. Texp Inverse calculation of ideal value Δ Tid Practical correction formula: ; Step S103: Determine the thermal conductivity of the gas being measured based on the corrected theoretical temperature rise data.

[0064] like Figure 2 As shown, in one embodiment, optionally, step S103 includes: Step S201: Based on the corrected theoretical temperature rise data, determine its logarithmic relationship with time.

[0065] The logarithm of time refers to the heating time expressed as the natural logarithm (ln) or the common logarithm (lg). Here, the natural logarithm (ln t) is preferred because the temperature rise and the logarithm of time have a linear relationship in the ideal heat conduction model.

[0066] The relationship between the theoretical temperature rise (ΔTid) and the logarithm of time (ln t) is a function that is usually presented as a curve.

[0067] This step is a preliminary step for identifying the ideal heat conduction period. The specific process is as follows: For the corrected theoretical temperature rise data ΔTid and the corresponding heating time t, the time t is converted into the natural logarithmic form (ln t), and a data set of "ΔTid - lnt" is constructed. The relationship curve between the two is plotted using data processing software. Theoretically, this curve should include three stages: the initial stage (heat capacity dominates, curve is nonlinear), the ideal heat conduction stage (one-dimensional radial heat conduction dominates, curve is linear), and the later stage (convection dominates, curve is nonlinear). The core purpose of this step is to intuitively present this change pattern in the form of a curve, laying the foundation for the subsequent identification of the linear segment.

[0068] In this way, the nonlinear relationship of "ΔTid - t" is transformed into an approximately linear relationship of "ΔTid - ln t", which conforms to the theoretical characteristics of the ideal heat conduction model, making the identification of the ideal heat conduction period more intuitive and easier to operate. The curve form can clearly present the characteristics of different heat transfer stages, avoiding the ambiguity of the ideal period caused by direct analysis of the original time data. At the same time, it provides a clear data carrier for subsequent differential geometric feature analysis, improving the accuracy of ideal period identification.

[0069] Step S202: Perform differential geometric feature analysis on the changing relationship to identify the ideal heat conduction time period that conforms to the one-dimensional radial unsteady-state heat conduction model; Differential geometric feature analysis refers to the method of analyzing the trend and stability of a curve by calculating its first derivative, second derivative, and other geometric parameters, and then determining the linear segment of the curve.

[0070] The one-dimensional radial unsteady-state heat conduction model is the core theoretical model of the transient hot-wire method. It assumes that heat is transferred only radially along the hot wire and that the measured medium is infinitely large, homogeneous, and isotropic. Under this model, the temperature rise has a strictly linear relationship with the logarithm of time.

[0071] The ideal heat conduction time period refers to the time period in the "ΔTid - ln t" curve that is strictly linear. During this period, heat transfer is mainly carried out by one-dimensional radial heat conduction, and the influence of heat capacity, convection, radiation and other interference factors can be ignored.

[0072] In this step, the linearity of the “ΔTid - ln t” curve is captured through differential geometric feature analysis. The specific process is as follows: first, the first derivative of the curve (reflecting the change in the curve slope) is calculated, and then the stability of the first derivative is analyzed to determine whether the curve is linear. During the ideal heat conduction period, the first derivative should remain constant (the curve slope remains unchanged), and the second derivative should approach zero. Through this analysis method, the interference of the initial heat capacity-dominated stage and the later convection-dominated stage can be automatically eliminated, and the time range corresponding to the linear segment that conforms to the ideal model can be accurately locked, ensuring that subsequent calculations are based only on reliable ideal heat conduction data.

[0073] In this way, differential geometric feature analysis enables the scientific identification of ideal heat conduction periods, avoiding the subjective errors of traditional manual selection of periods and improving the consistency and reliability of period identification. By focusing on the ideal period dominated by one-dimensional radial heat conduction, it ensures that the data used to calculate the thermal conductivity can truly reflect the thermal conductivity characteristics of the measured gas, eliminates the interference of non-ideal heat transfer stages, further reduces system errors, and provides a key guarantee for high-precision calculation of thermal conductivity.

[0074] Step S203: Calculate the slope value corresponding to the change relationship within the ideal heat conduction time period; The slope value refers to the linear fitting slope of the "ΔTid - ln t" curve within the ideal heat conduction time period. It is the core intermediate parameter for calculating the thermal conductivity. In the ideal model, the slope is inversely proportional to the thermal conductivity.

[0075] Extract the data points "ΔTid - ln t" within the ideal heat conduction time period, and use the least squares method to perform linear regression fitting on these data points to obtain the equation of the fitted line (ΔTid = k). (ln t + b, where k is the slope and b is the intercept); during the fitting process, the correlation coefficient R² needs to be calculated to verify the linearity of the data points and ensure the reliability of the slope value; if there are multiple time periods with the required linearity, the slope value corresponding to the longest time period is selected as the final calculation basis.

[0076] The application of the least squares method ensures the accuracy of slope calculation, and compared with the simple two-point calculation method, it can effectively offset the random error of data points. The verification of the correlation coefficient further ensures the reliability of the slope value and avoids calculation deviation caused by insufficient linearity. As the core parameter connecting the theoretical model and the final measurement result, the accurate calculation of the slope value provides direct support for the accurate output of the thermal conductivity. At the same time, the automation of this process improves the operational efficiency of the method.

[0077] Step S204: Calculate the thermal conductivity of the gas being tested based on the slope value and the heating power of the hot wire.

[0078] The heating power of a hot wire refers to the constant output of heat per unit length of hot wire during the heating process, denoted as q, which is a known parameter set and calibrated before the experiment.

[0079] In one embodiment, optionally, the thermal conductivity of the gas being measured is calculated using the following formula: λ = q / (4πk); Wherein, λ represents the thermal conductivity of the gas being measured, q represents the heating power of the hot wire, and k represents the slope value.

[0080] Based on the theoretical derivation of a one-dimensional radial unsteady-state heat conduction model, the slope value k and the known heating power q per unit length are substituted into the formula for calculating the thermal conductivity. The thermal conductivity λ of the gas being measured can be obtained through simple algebraic operations. The calculation process can be completed automatically by data processing software without manual intervention, and the output results can be directly used for engineering applications or further data verification.

[0081] In this way, based on the slope value of the ideal heat conduction period and the known heating power, the thermal conductivity is directly calculated through theoretical formulas, ensuring the scientific nature and accuracy of the results. The calculation process is simple, efficient, and highly automated, avoiding complex manual calculations and empirical corrections, thus improving the engineering applicability of the method. At the same time, the calculation logic is highly consistent with the ideal model, enabling the final output thermal conductivity to truly reflect the thermophysical properties of the measured gas, meeting the needs of fields such as nuclear engineering for high-precision data.

[0082] like Figure 3 As shown, in one embodiment, optionally, step S202 includes: Step S301: Calculate the first derivative curve of the corrected theoretical temperature rise data with respect to the logarithm of time, as shown below. Figure 4 As shown; The corrected data is transformed to obtain the ΔTid~lnt relationship curve. According to the ideal model, the slope of this curve is k= ΔTid / lnt should be a constant. Therefore, we first calculate the first derivative (i.e., the numerical differential) of the curve. ΔTid / lnt and obtain the first derivative-time logarithm relationship curve.

[0083] Step S302: Using multiple data windows of different widths, recursive piecewise linear fitting is performed on the first derivative curve; This step aims to scan the entire first derivative curve to find the flattest and most stable segment. Setting the fitting window: A set of preset fitting window sizes, ranging from small to large (containing windows of 20, 50, 100, 200, 300, and 400 consecutive data points), is used. Different window sizes are used to explore the linear characteristics of the curve at different scales. Recursive linear fitting: For each specified window size (e.g., a 200-point window), starting from the beginning of the data, linear fitting is performed sequentially on the data points within the window using a sliding window approach to obtain the slope of that local window (i.e., the local average of the first derivative).

[0084] The data window refers to the size of the set of data points used for linear fitting (i.e., the number of data points contained in the window), such as 20 points, 50 points, 100 points, etc.

[0085] Recursive piecewise linear fitting refers to the process of moving a data window with a fixed step size (usually 1 data point), performing linear fitting on the first derivative data within each window, and obtaining the fitting slope and intercept corresponding to each window.

[0086] The multi-window design overcomes the limitations of a single window size, adapts to the differences in the ideal heat conduction time length under different measurement scenarios, and improves the versatility of the method. The recursive piecewise fitting realizes full-range coverage analysis of the first derivative curve, avoids the omission of local features, and the automation of the fitting process ensures the consistency of the analysis results, providing comprehensive and reliable basic data for the subsequent second derivative stability judgment.

[0087] Step S303: Calculate the second derivative of the fitted line segment within each data window, such as... Figure 5 As shown.

[0088] For the new relationship curve formed by each window center point and its fitted slope, recalculate the rate of change of its slope, i.e., the second derivative. 2ΔTid / lnt2. In an ideal linear segment, the first derivative is constant, and its second derivative should theoretically be zero.

[0089] The second derivative transforms the stability of the first derivative into a quantifiable numerical indicator, upgrading the judgment of the ideal heat conduction period from intuitive observation to quantitative analysis, thus improving the scientific nature and accuracy of the judgment. By calculating the second derivative of each window, the start and end positions of the ideal heat conduction period can be accurately located, avoiding deviations caused by subjective judgment, and providing core data support for subsequent variance analysis.

[0090] Step S304: Using the variance of the second derivative as the criterion, the longest continuous data segment with the smallest variance of the second derivative that is close to zero is identified as the ideal heat conduction time period.

[0091] Calculate the statistical characteristics of the second derivative obtained under each fitting window, such as variance or standard deviation. The smaller the variance, the more stably the second derivative approaches zero within that window, meaning the curve is flatter. The system automatically finds the longest continuous data sequence with the smallest variance of the second derivative approaching zero. The start and end points of this sequence are determined as the start and end points of the ideal heat conduction period. When using optimal window fitting, the second derivative curve exhibits a clearly flat, near-zero plateau region within the ideal period.

[0092] The variance of the second derivative refers to the degree of dispersion of the second derivative of a set of continuous data windows. The smaller the variance, the more stable the second derivative is, and the closer it is to zero. The longest continuous data segment refers to the time range corresponding to the continuous data window where the variance of the second derivative satisfies the condition of being minimal and approaching zero. This range is the ideal heat conduction period.

[0093] In this way, by using the variance of the second derivative as the criterion, the ideal heat conduction period can be quantitatively identified, avoiding subjective errors and improving the reliability and repeatability of the identification results. The dual criteria of minimum variance and longest continuity ensure both the stability of the period (conforming to the ideal model) and the length of the period (meeting the fitting accuracy requirements), thus providing the optimal data segment for subsequent slope calculation and thermal conductivity output, further improving the accuracy of the measurement results.

[0094] In one embodiment, optionally, the gas being measured includes helium, and the measurement temperature is above 500 degrees Celsius.

[0095] In this embodiment, helium is used as a typical representative of the measured gas (but other gases can also be adapted), and the measurement environment focuses on high-temperature scenarios above 500°C. In this scenario, the thermal properties of helium cause more pronounced deviations in traditional ideal models, and existing correction methods are often unsuitable. This method effectively solves the measurement challenges at high temperatures through targeted corrections based on thermal capacity and radiative heat transfer, as well as precise identification of ideal time periods. During application, it is necessary to ensure that the experimental setup can withstand the high-temperature environment, and that the temperature sensor and heating system maintain stable performance at high temperatures. This specifically addresses the industry pain point of insufficient accuracy in measuring the thermal conductivity of gases such as helium at high temperatures, filling a gap in existing technologies for high-temperature gas measurement. The adaptability to high-temperature environments expands the method's engineering application scope, meeting the special measurement needs of fields such as nuclear engineering and high-temperature energy systems, providing reliable data support for heat transfer design and safety assessment in these fields, and also demonstrating the technical advantages of this method in scenarios significantly affected by non-ideal factors.

[0096] Figure 6 A block diagram of a gas thermal conductivity measurement data processing apparatus based on the hot-wire method according to an embodiment of this application is shown.

[0097] like Figure 6 As shown, in a second aspect, embodiments of this application provide a gas thermal conductivity measurement data processing device 60 based on the hot-wire method, comprising: The acquisition module 61 is used to acquire experimental data on the change of hot wire temperature rise over time, which is obtained by the transient hot wire method experiment. The correction module 62 is used to correct the experimental data of the hot wire temperature rise through a theoretical correction model to obtain the corrected theoretical temperature rise data. The theoretical correction model is derived based on a physical model that considers the finite heat capacity of the hot wire and the radiative heat transfer effect. The determination module 63 is used to determine the thermal conductivity of the gas being measured based on the corrected theoretical temperature rise data.

[0098] In one embodiment, optionally, the representation of the theoretical correction model includes: ΔTid =ΔTexp + C1·f1(t) - C2·f2(t); Wherein, ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the experimental temperature rise data of the hot wire, C1·f1(t) represents the compensation term corresponding to the finite heat capacity effect of the hot wire, where C1 represents the heat capacity correction coefficient related to the heat capacity of the hot wire, f1(t) represents the function related to the experimental temperature rise rate, C2·f2(t) represents the deduction term corresponding to the radiative heat transfer effect, C2 represents the radiation correction coefficient related to radiative heat transfer, and f2(t) represents the function related to time.

[0099] In one embodiment, the optional theoretical correction model specifically includes: ; Where ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the experimental temperature rise data of the hot wire, r0 represents the radius of the hot wire, t represents time, and ε p Let ρ represent the emissivity of the hot wire, σ represent the Stefan-Boltzmann constant, T(r0,0) represent the initial surface temperature of the hot wire, and ρ represent the emissivity of the hot wire. w c represents thermal density. pw λ represents the specific heat capacity of the hot wire at constant pressure, λ represents the thermal conductivity, and T∞ represents the ambient temperature at the initial equilibrium state.

[0100] In one embodiment, optionally, the determining module includes: The relationship determination unit is used to determine the logarithmic relationship between the corrected theoretical temperature rise data and time. The identification unit is used to perform differential geometric feature analysis on the changing relationship and identify the ideal heat conduction time period that conforms to the one-dimensional radial unsteady heat conduction model. The first calculation unit is used to calculate the slope value corresponding to the change relationship during the ideal heat conduction time period; The second calculation unit is used to calculate the thermal conductivity of the gas being measured based on the slope value and the heating power of the hot wire.

[0101] In one embodiment, optionally, the identification unit is specifically used for: Calculate the first derivative curve of the logarithm of the corrected theoretical temperature rise data with time; The first derivative curve is recursively piecewise linearly fitted using multiple data windows of different widths. Calculate the second derivative of the fitted line segment within each data window; Using the variance of the second derivative as the criterion, the longest continuous data segment with the smallest variance of the second derivative that approaches zero is identified as the ideal heat conduction time period.

[0102] In one embodiment, optionally, the thermal conductivity of the gas being measured is calculated using the following formula: λ = q / (4πk); Wherein, λ represents the thermal conductivity of the gas being measured, q represents the heating power of the hot wire, and k represents the slope value.

[0103] In one embodiment, optionally, the gas being measured includes helium, and the measurement temperature is above 500 degrees Celsius.

[0104] In the above-described scheme for processing gas thermal conductivity measurement data based on the hot-wire method, the experimental data of the hot-wire temperature rise over time, measured by the transient hot-wire method, are obtained. The experimental data is then corrected using a theoretical correction model to obtain corrected theoretical temperature rise data. This theoretical correction model is derived from a physical model that considers the finite heat capacity of the hot wire and the effect of radiative heat transfer. Based on the corrected theoretical temperature rise data, the thermal conductivity of the gas being measured is determined. By acquiring experimental data on the temperature rise of the hot wire as a function of time obtained from the transient hot wire method, and using a theoretical correction model derived from a physical model that considers the finite heat capacity of the hot wire and the effect of radiative heat transfer, the experimental data is precisely corrected. This effectively offsets the core non-ideal factors in the ideal model of the traditional transient hot wire method, such as the temperature rise delay caused by the heat capacity of the hot wire itself and the temperature rise being too low due to radiative heat loss. The biased experimental data is transformed into theoretical temperature rise data that conforms to the ideal thermal conductivity model. Then, the thermal conductivity of the gas being measured is determined based on the corrected reliable data. This not only overcomes the limitations of existing correction methods, which are mostly applicable to liquid working fluids and cannot be adapted to gaseous working fluids (such as helium), but also significantly improves the accuracy and reliability of gas thermal conductivity measurement. Furthermore, it simplifies the data processing flow, avoids subjective errors caused by human intervention, and ensures the consistency and repeatability of results under different measurement scenarios. This provides reliable data support for fields such as nuclear engineering that have stringent requirements for the accuracy of gas thermal conductivity, and reduces engineering risks caused by data deviations.

[0105] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described gas thermal conductivity measurement data processing method based on the hot-wire method.

[0106] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described gas thermal conductivity measurement data processing method based on the hot-wire method.

[0107] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the gas thermal conductivity measurement data processing device and its modules described above based on the hot-wire method can be referred to the corresponding process in the aforementioned embodiments of the gas thermal conductivity measurement data processing method based on the hot-wire method, and will not be repeated here.

[0108] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the model training device and each module described above can be referred to the corresponding process in the aforementioned embodiment of the gas thermal conductivity measurement data processing method based on the hot wire method, and will not be repeated here.

[0109] The aforementioned data processing device for measuring the thermal conductivity of gases based on the hot-wire method can be implemented as a computer program, which can be used in various ways, such as... Figure 7 It runs on the computer device shown.

[0110] Figure 7 A block diagram of a computer device according to one embodiment of this application is shown.

[0111] See Figure 7 The computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include storage media and internal memory.

[0112] The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the gas thermal conductivity measurement data processing methods based on the hot-wire method provided in the embodiments of this application.

[0113] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0114] The internal memory provides an environment for the execution of a computer program stored in the storage medium. When executed by a processor, this program enables the processor to perform any multi-source data processing method for measuring the thermal conductivity of gases based on the hot-wire method. The storage medium can be non-volatile or volatile.

[0115] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0117] In addition, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the steps of the method in the first aspect embodiment.

[0118] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or electronic device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0119] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0120] It should be understood that although the terms "first," "second," etc., may be used to describe the setting units in the embodiments of this application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of this application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0121] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0123] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0124] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0125] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for processing data from gas thermal conductivity measurement, characterized in that, The method includes: Obtain experimental data on the change of hot wire temperature rise over time, measured by the transient hot wire method. The experimental data of the hot wire temperature rise were corrected by a theoretical correction model to obtain the corrected theoretical temperature rise data. The theoretical correction model was derived based on a physical model that considers the finite heat capacity of the hot wire and the radiative heat transfer effect. Based on the corrected theoretical temperature rise data, the thermal conductivity of the gas being measured is determined.

2. The method according to claim 1, characterized in that, The theoretical correction model is represented as follows: ΔTid =ΔTexp + C1·f1(t) - C2·f2(t); Wherein, ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the experimental temperature rise data of the hot wire, C1·f1(t) represents the compensation term corresponding to the finite heat capacity effect of the hot wire, where C1 represents the heat capacity correction coefficient related to the heat capacity of the hot wire, f1(t) represents the function related to the experimental temperature rise rate, C2·f2(t) represents the deduction term corresponding to the radiative heat transfer effect, C2 represents the radiation correction coefficient related to radiative heat transfer, and f2(t) represents the function related to time.

3. The method according to claim 2, characterized in that, The theoretical correction model specifically includes: ； Where ΔTid represents the corrected theoretical temperature rise data, ΔTexp represents the experimental temperature rise data of the hot wire, r0 represents the radius of the hot wire, t represents time, and ε p Let ρ represent the emissivity of the hot wire, σ represent the Stefan-Boltzmann constant, T(r0,0) represent the initial surface temperature of the hot wire, and ρ represent the emissivity of the hot wire. w c represents the thermal density. pw λ represents the specific heat capacity of the hot wire at constant pressure, λ represents the thermal conductivity, and T∞ represents the ambient temperature at the initial equilibrium state.

4. The method according to claim 1, characterized in that, Based on the corrected theoretical temperature rise data, the thermal conductivity of the gas being measured is determined, including: Based on the corrected theoretical temperature rise data, determine its logarithmic relationship with time; Differential geometric feature analysis was performed on the aforementioned change relationship to identify the ideal heat conduction time period that conforms to the one-dimensional radial unsteady-state heat conduction model; Calculate the slope value corresponding to the change relationship within the ideal heat conduction time period; The thermal conductivity of the gas being tested is calculated based on the slope value and the heating power of the hot wire.

5. The method according to claim 4, characterized in that, Differential geometric feature analysis is performed on the aforementioned changing relationship to identify the ideal heat conduction time period that conforms to the one-dimensional radial unsteady-state heat conduction model, including: Calculate the first derivative curve of the logarithm of the corrected theoretical temperature rise data with time; The first derivative curve is recursively piecewise linearly fitted using multiple data windows of different widths. Calculate the second derivative of the fitted line segment within each data window; Using the variance of the second derivative as the criterion, the longest continuous data segment with the smallest variance of the second derivative that approaches zero is identified as the ideal heat conduction time period.

6. The method according to claim 4, characterized in that, The thermal conductivity of the gas being measured is calculated using the following formula: λ = q / (4πk); Wherein, λ represents the thermal conductivity of the gas being measured, q represents the heating power of the hot wire, and k represents the slope value.

7. The method according to claim 4, characterized in that, The gas being measured includes helium, and the measurement temperature is above 500 degrees Celsius.

8. A data processing device for measuring the thermal conductivity of a gas, characterized in that, include: The acquisition module is used to acquire experimental data on the change of hot wire temperature rise over time, which is obtained by the transient hot wire method experiment. The correction module is used to correct the experimental data of the hot wire temperature rise through a theoretical correction model to obtain the corrected theoretical temperature rise data. The theoretical correction model is derived based on a physical model that considers the finite heat capacity of the hot wire and the radiative heat transfer effect. The determination module is used to determine the thermal conductivity of the gas being measured based on the corrected theoretical temperature rise data.

9. A computer device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 7.

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