A thermal diffusivity measurement method considering spatial heat loss

Abstract

The present invention belongs to the field of infrared heat wave technology, and provides a method for measuring thermal diffusivity taking into account spatial heat loss, thereby being able to measure a very accurate thermal diffusivity coefficient of a material. The present invention includes the following steps: Step S1: Keeping the front surface of a sample specimen to be tested and a light source emitter at the same horizontal position, so that the modulated thermal light source emitted by the light source emitter can completely irradiate the front surface of the sample specimen to be tested; Step S2: Setting modulation signals with different modulation frequencies through the light source emitter, respectively applying thermal excitations of different modulation frequencies to the front surface of the sample to be tested, so that the sample generates heat diffusion inside and generates thermal radiation on the surface; Step S3: Using an infrared thermal imager to collect and record the temperature field data of the upper surface of the sample specimen at different modulation frequencies; Step S4: Establishing a thermal model; Step S5: Extracting the temperature response phase and amplitude depth information, and obtaining the temperature field data distribution results that vary with depth at different modulation frequencies.

Description

Technical Field

[0001] The present invention belongs to the field of infrared heat wave technology, and in particular, relates to a thermal diffusivity measurement method taking into account spatial heat loss. Background Art

[0002] Thermal diffusivity is one of the important parameters of material heat transfer performance, which describes the ability of a material to conduct heat under a temperature gradient. Materials with high thermal diffusivity can conduct heat quickly, while materials with low thermal diffusivity conduct heat slowly. Currently, in many industrial and scientific application fields, such as electronic equipment heat dissipation, thermal management systems, and material science research, very accurate material thermal diffusivity has a crucial impact on the operation and heat dissipation capabilities of its equipment or system. Therefore, being able to accurately measure the thermal diffusivity of a material is of great guiding significance for subsequent work.

[0003] Traditional material thermal diffusivity measurement methods are mainly divided into two categories: steady-state method and non-steady-state method. The steady-state method refers to calculating the thermal diffusivity by measuring the temperature gradient and heat flux density after the system reaches a steady-state condition (that is, the temperature distribution no longer changes with time). It is an indirect measurement method, such as the flat plate measurement method. However, the steady-state method requires a long time to wait for the system to reach a steady-state condition, and indirectly calculates the thermal diffusivity by measuring the temperature gradient and heat flux density. The steady-state method is not suitable for all materials, especially materials with low thermal conductivity, and its measurement process is long and not accurate enough.

[0004] The non-steady-state method refers to the system being in a non-steady-state condition, using the thermal response characteristics of the material when subjected to instantaneous or periodic thermal disturbances, and directly calculating the thermal diffusivity by measuring the temperature response of the material under instantaneous or periodic thermal disturbances. The advantages of the non-steady-state method are fast measurement speed and wide application range. Common non-steady-state methods include laser flash method, periodic heating method and thermal wave method. The laser flash method heats the sample surface by applying an instantaneous laser pulse to one side of the sample, measures the temperature response curve of the heat pulse transmitted to the other side of the sample, and uses the one-dimensional heat conduction equation to analyze the thermal diffusivity. Its advantages are high measurement accuracy, fast test speed, and applicability to high temperature conditions. However, its disadvantages are that it requires high-precision equipment and stricter sample preparation requirements. The principle of the periodic heating method is to use the periodic heating and thermal response of the sample surface, and analyze the amplitude and phase change of temperature fluctuations through Fourier transform to calculate the thermal diffusivity. Its advantages are that it can measure multiple thermophysical parameters at the same time and is applicable to different frequency ranges. Its disadvantages are that it requires complex data processing and requires high measurement accuracy. The principle of the thermal wave method is to use the propagation characteristics of thermal waves in materials, measure the attenuation and phase shift of thermal waves to calculate the thermal diffusivity. Its advantages are that it is applicable to thin film materials and can measure local thermal physical parameters. Its disadvantages are that the experimental device is complex and has high requirements for environmental stability.

[0005] Lock-in Thermography (LIT) is an advanced non-steady-state measurement technology. The measurement principle of lock-in thermography is based on the fact that materials will generate thermal waves under periodic thermal excitation. The propagation speed and attenuation characteristics of thermal waves in materials are closely related to the thermal diffusivity of the materials. By measuring the temperature distribution on the surface of the sample and performing phase-locked measurement of the temperature response, the phase and amplitude information of the thermal wave can be obtained. The frequency domain representation of the temperature response is analyzed by Fourier transform, the phase difference and amplitude attenuation are extracted, and the thermal diffusivity of the material is calculated using the heat conduction equation.

[0006] However, in traditional heat conduction research, for heat dissipation on the solid boundary surface, the heat loss coefficient is often considered or assumed to be a constant that is independent of temperature or coordinates. All of the above thermal diffusion coefficient measurement methods do not consider the influence of material spatial heat loss. In practical problems, the heat loss coefficient of the material surface changes with depth. This change will lead to inaccurate frequency-related material thermal diffusion coefficients. However, in existing models, there is no calculation model that considers spatial heat loss. Summary of the invention

[0007] The purpose of the present invention is to provide a thermal diffusivity measurement method taking into account the heat loss on the surface of the material, thereby measuring the thermal diffusivity coefficient of the material very accurately.

[0008] In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0009] The present invention comprises the following steps:

[0010] Step S1: Keep the front surface of the sample to be tested and the light source emitter at the same horizontal position, so that the modulated thermal light source emitted by the light source emitter can completely irradiate the front surface of the sample to be tested;

[0011] Step S2: setting modulation signals with different modulation frequencies through the light source transmitter, and applying thermal excitations with different modulation frequencies to the front surface of the sample to be tested, so that the sample generates heat diffusion inside and heat radiation on the surface;

[0012] Step S3: using an infrared thermal imager to collect and record temperature field data of the upper surface of the sample specimen at different modulation frequencies;

[0013] Step S4: establishing a thermal model;

[0014] Step S5: extracting the temperature response phase and amplitude depth information to obtain the temperature field data distribution results that vary with depth at different modulation frequencies;

[0015] Step S6: fitting the temperature field data distribution results under different modulation frequencies to obtain the individual best fitting heat loss coefficient model parameters and the individual best fitting thermal diffusion coefficient under different modulation frequencies;

[0016] Step S7: According to the individual optimal fitting parameters under different modulation frequencies, the global optimal fitting heat loss coefficient model parameters and the global optimal fitting thermal diffusion coefficient are obtained, and it is determined whether the global optimal fitting heat loss coefficient model parameter value meets the residual minimization convergence criterion. If it does, the iteration ends, and the global optimal fitting thermal diffusion coefficient is output to obtain the thermal diffusivity of the sample; if it does not meet the criterion, the iteration continues, and the heat loss coefficient model parameter value is further adjusted, and steps S6 and S7 are continued until the iteration ends.

[0017] As a preferred solution, in step S1 of the present invention, based on the phase-locked thermal imaging measurement method, the sample is fixed on the experimental table, the sample is stable and does not move, and an appropriate distance is maintained between the sample specimen and the thermal excitation source and the infrared thermal imager to ensure the uniformity of the thermal excitation and the field of view of the infrared thermal imager covers the sample surface.

[0018] As another preferred embodiment, the "appropriate distance" described in the present invention is adjusted according to the sample size and maintained between 20 cm and 50 cm.

[0019] As another preferred solution, the present invention provides a window-shaped opening so that the thermal excitation source can only irradiate and cover the front surface of the sample specimen after passing through the window-shaped opening, and the other surfaces of the specimen are not irradiated by the heat source.

[0020] As another preferred embodiment, in step S2 of the present invention, periodic thermal excitation is applied to the surface of the sample specimen, and the frequency and power of the thermal excitation source are adjusted to allow the thermal excitation to propagate in the sample specimen.

[0021] As another preferred solution, the thermal excitation frequency of the present invention ranges from 0.01 Hz to 10 Hz. The specific frequency should be selected according to the material and thickness of the sample.

[0022] As another preferred solution, the thermal excitation signal of the present invention adopts a periodic square wave with a duty cycle of 50%. For the periodic thermal excitation signal, compared with the sine-cosine waveform, the periodic square wave excitation signal with a duty cycle of 50% can effectively improve the signal-to-noise ratio.

[0023] As another preferred solution, the minimum acceptable power range of the laser used as thermal excitation in the present invention should be between 5 W and 15 W. 5 W is preferred to reduce the repeatability of detection and achieve good imaging quality.

[0024] As another preferred solution, in step S3 of the present invention, an infrared thermal imager is used to record the temperature distribution on the surface of the sample specimen, ensuring that the complete temperature response during the thermal excitation cycle is captured. Before the measurement, the infrared thermal imager is calibrated on the specimen to ensure the accuracy of the measurement.

[0025] As another preferred solution, the present invention ensures that the surface temperature of the sample reaches a stable state before recording the temperature data of the sample specimen with an infrared thermal imager.

[0026] As another preferred solution, in order to ensure that the surface temperature of the sample specimen reaches a stable state, the laser excitation time of the sample front surface is maintained at 10 minutes to 20 minutes, preferably 15 minutes.

[0027] As another preferred solution, step S4 of the present invention specifically includes the following steps:

[0028] Step S4-1: Consider the steady heat loss of the sample specimen material and establish a new two-dimensional space heat wave equation that changes with time; the traditional two-dimensional heat conduction equation does not consider the concept of spatial heat loss and often sets the heat loss coefficient as a constant. In the method proposed by the present invention, the steady heat loss is taken into account in the traditional heat conduction equation to obtain a new two-dimensional heat conduction equation.

[0029] Step S4-2: transforming the obtained two-dimensional space heat wave equation into the time-frequency domain to obtain a one-dimensional space heat wave equation in the frequency domain, and then obtaining a steady heat flux equation under steady heat loss conditions;

[0030] Step S4-3: construct a spatial heat loss model. The spatial heat loss and the depth of the sample specimen are interdependent, and the thermal diffusion coefficient decreases with increasing depth;

[0031] Step S4-4: using a power series solution method to obtain a generalized solution of the thermal wave field with spatial heat loss;

[0032] Step S4-5: By comparing and determining the expressions of the generalized solution coefficients, a thermal wave field solution with spatially dependent heat loss coefficients and depth variations can be obtained.

[0033] As another preferred embodiment, the steady-state heat loss expression in step S4-1 of the present invention is constructed as follows:

[0034] Among them, h represents the heat loss coefficient of the material, k represents the thermal conductivity of the material, C represents the geometric perimeter of the front surface of the sample C=2(B+W), S represents the area of ​​the front surface of the sample S=BW, B represents the height of the sample, and W represents the width of the sample.

[0035] As another preferred solution, in step S4-2 of the present invention, the two-dimensional space thermal wave equation obtained in step S4-1 is still a function related to temperature and time, and a Fourier transform is performed on it to obtain a frequency domain expression under temperature response, and then a steady heat flux expression T(x,ω) under steady heat loss conditions is obtained:

[0036]

[0037] The front surface of the sample (x = 0, cross-sectional area S) is illuminated by a modulated beam, and the flux generated is defined as q 0 ; The heat loss coefficient of the front surface (x = 0) is h s0 ; The leakage heat wave number is defined as σ represents the attenuation constant of the thermal wave, m represents the thermal wave number, and σ 2 +m 2 represents the overall characteristics of the heat wave when it propagates in the material, e iωt It represents the corresponding relationship of temperature transformation from time domain to frequency domain.

[0038] As another preferred embodiment, in step S4-3 of the present invention, when the temperature of the specimen reaches a stable state, the spatial heat loss coefficient of the sample specimen at the entire depth is further considered. The spatial heat loss coefficient and the depth x have a polynomial dependency relationship, and the spatial heat loss model expression is constructed:

[0039]

[0040] Among them, h n , (n=0,1,…,n) represents the heat loss coefficient along the side wall.

[0041] Preferably, the quadratic spatial heat loss model H(x)=H 0 +H 1 x+H 2 x 2 It is used to fit the experimental data optimally, where H n =h n +(W / B)h sn .

[0042] As another preferred solution, in step S4-4 of the present invention, a power series solution method is used to bring the spatial heat loss model expression into the frequency domain expression under the temperature response obtained in step S4-2, and further obtain a generalized solution of the thermal wave field with spatial heat loss:

[0043]

[0044] Among them, a n is a system of algebraic equations with a set of solutions.

[0045] As another preferred solution, in step S4-5 of the present invention, the generalized solution of the thermal wave field of spatial heat loss in step S4-4 is brought into the frequency domain expression under the temperature response obtained in step S4-2, and after performing algebraic operations, an, (n = 0, 1, ..., n) and a are obtained. 0 The associated algebraic equations give the final thermal wave field solution T(x,ω) of the spatial heat loss coefficient varying with depth:

[0046]

[0047] Among them, the leakage heat wave number considering the space heat loss is defined as It changes with the heat loss value of the space.

[0048] Secondly, in step S5 of the present invention, the data collected at different modulation frequencies are processed by phase-locked digital technology to obtain the temperature response phase and amplitude information associated with the depth, and then obtain the phase temperature field data distribution results associated with the depth and the amplitude temperature field data distribution results associated with the depth.

[0049] In addition, in step S7 of the present invention, the Parteo frontier is calculated based on the individual optimal fitting parameters at each modulation frequency to obtain the global optimal fitting parameter range, and the individual optimal fitting parameters at each frequency are further comprehensively fitted to obtain the global optimal fitting heat loss coefficient model parameters and the global optimal fitting thermal diffusion coefficient, thereby obtaining the thermal diffusivity of the sample.

[0050] Compared with the existing model method, the model algorithm proposed by the present invention has the following beneficial effects:

[0051] 1. The thermal wave field calculation model that varies with depth proposed in the present invention innovatively introduces the concept of a spatial heat loss model for the first time. The heat loss coefficient and depth will affect the amplitude and phase of the thermal wave field.

[0052] 2. The model proposed by the present invention can characterize the depth dependence of the heat loss coefficient. The results of the attached figure confirm that the heat loss coefficient changes with depth, which is different from the traditional assumption of a constant heat loss coefficient. The model proposed by the present invention fills the gap in this field.

[0053] 3. The model proposed in the present invention can further improve and optimize the accuracy of the measurement results of the thermophysical parameters of the material (mainly the thermal diffusion coefficient), and can avoid the measurement error caused by ignoring the change of the heat loss coefficient with depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1This is a flow chart of a specific implementation of a thermal diffusivity measurement method considering spatial heat loss provided by the present invention.

[0055] Figure 2 This is a schematic diagram of sample parameters and coordinate parameters provided by the present invention.

[0056] Figure 3 This is a surface diagram of the change of the logarithmic amplitude at different heat loss coefficients and depths provided in this embodiment.

[0057] Figure 4 This is a surface diagram of the phase variation at different heat loss coefficients and depths provided in this embodiment.

[0058] Figure 5 This is a surface diagram of the change of the logarithmic amplitude provided in this embodiment at the same heat loss coefficient and depth.

[0059] Figure 6 This is a surface diagram of the phase change at the same heat loss coefficient and depth provided in this embodiment. DETAILED DESCRIPTION

[0060] In order to better illustrate the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings, but it is not limited to this. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0061] Figure 1 This is a flow chart of a specific implementation of a method for measuring thermal diffusivity taking into account spatial heat loss provided by the present invention. Figure 1 As shown, a thermal diffusivity measurement method considering spatial heat loss in this embodiment is based on a phase-locked thermal imaging measurement method and is performed in the following steps:

[0062] Step S1: The sample is a metal powder compacted thin plate. First, fix the sample on the experimental table and ensure that the sample is stable and will not move. Keep the front surface of the sample to be tested and the light source emitter (JOLD-75-CPXF-2P semiconductor fiber coupler can be used) in a uniform horizontal position so that the modulated thermal light source can fully illuminate the front surface. Keep an appropriate distance between the sample and the thermal excitation source and the infrared thermal imager (the distance between the metal powder compacted thin plate and the thermal excitation source and the infrared thermal imager is kept between 20cm and 50cm) to ensure the uniformity of thermal excitation and the field of view of the infrared thermal imager covers the sample surface.

[0063] A window is designed to ensure that the thermal excitation source can only irradiate and cover the front surface of the metal powder compacted sheet specimen after passing through the window, and no heat source is irradiated on the other surfaces of the specimen. The size of the window is set according to the size of the front surface of the sample specimen, and is arranged between the sample specimen and the light source emitter and close to the sample specimen. The window is generally kept between 5cm and 15cm away from the sample specimen.

[0064] If the surface of the sample to be tested is not sensitive to the radiation of the infrared thermal imager, apply a thin and uniform black coating on the surface of the sample (graphite spray agent can be used. Graphite has good infrared radiation absorption characteristics and is easy to spray. The coating thickness is generally controlled within the range of 10 to 20 microns) to improve the absorption and emission efficiency of thermal radiation.

[0065] Before conducting the experiment, it is necessary to accurately measure the geometric parameters of the metal powder compacted sheet sample: such as depth (length), width, and height. Figure 2 As shown, the metal powder compacted sheet of this embodiment has a depth (length) L=31 mm, a width W=6 mm, and a height B=12 mm.

[0066] Step S2: Set modulation signals with different modulation frequencies, apply periodic thermal excitation to the sample surface, and adjust the power of the thermal excitation source to ensure that the thermal excitation can be effectively propagated in the sample specimen. Specifically, for the metal powder compacted thin plate sample of this embodiment, a laser thermal excitation source is used. In order to reduce the repeatability of detection and achieve good imaging quality, the minimum acceptable power of the laser thermal excitation is preferably 5W. For the periodic excitation signal, compared with the sine-cosine waveform, a periodic square wave excitation signal with a duty cycle of 50% can effectively improve the signal-to-noise ratio. Therefore, this embodiment preferably uses a periodic square wave with a duty cycle of 50% as the excitation signal. In addition, the commonly used thermal excitation frequency modulation range is generally between 0.01Hz and 10Hz. For different modulation frequency selections of the laser heat source, this embodiment selects five modulation frequencies of 0.01Hz, 0.05Hz, 0.1Hz, 0.5Hz and 1Hz according to the material and thickness of the metal powder compacted thin plate.

[0067] Step S3: Before the experiment is carried out, the infrared thermal imager needs to be calibrated for the specimen to ensure the accuracy of the measurement. During the experiment, the infrared thermal imager is used to record the temperature distribution of the sample surface over a period of time to ensure that the complete temperature response during the thermal excitation cycle is captured. In this embodiment, for the metal powder compacted thin plate sample, before the infrared thermal imager is used to record the temperature data of the sample, the laser is used to excite the front surface of the metal powder compacted thin plate sample for 10 minutes to 20 minutes, preferably 15 minutes, to ensure that the surface temperature of the sample reaches a stable state.

[0068] Step S4: Establishing a thermal model, including the following steps:

[0069] Step S4-1: The traditional two-dimensional heat conduction equation does not consider the concept of spatial heat loss, and often sets the heat loss coefficient as a constant. In this embodiment, the steady heat loss of the metal powder compacted sheet material is taken into account, and a new two-dimensional space heat wave equation that changes with time is established, in which the expression of the steady heat loss is constructed as follows:

[0070] Among them, h represents the heat loss coefficient of the material, k represents the thermal conductivity of the material, C represents the geometric perimeter of the front surface of the sample C = 2 (B + W), S represents the area of ​​the front surface of the sample S = BW, B represents the height of the sample, and W represents the width of the sample. The specific settings are as follows Figure 2 shown.

[0071] Step S4-2: The new two-dimensional heat conduction equation obtained in step S4-1 is still a function related to temperature and time. A Fourier transform is performed on it to obtain the one-dimensional heat wave equation expression of temperature response in the frequency domain, and then the steady-state heat flux expression T(x,ω) under steady-state heat loss conditions can be obtained:

[0072]

[0073] The front surface of the sample (x = 0, the cross-sectional area is S) is illuminated by a modulated beam, and the flux generated is defined as q 0 ; The heat loss coefficient of the front surface (x = 0) is h s0 ; The leakage heat wave number is defined as σ represents the attenuation constant of the thermal wave, m represents the thermal wave number, and σ 2 +m 2 Represents the overall properties of heat waves as they propagate through a material.

[0074] Figure 3 : This is a surface diagram of the change of logarithmic amplitude at different heat loss coefficients and depths provided in this embodiment. In the figure, the logarithmic amplitude at a fixed depth (length) position, the amplitude attenuation decreases as the heat loss coefficient increases. There is a very obvious nonlinear relationship between the logarithmic amplitude and the heat loss coefficient, and the rate of change of the logarithmic amplitude increases as the heat loss coefficient increases. Figure 3 The effectiveness of the thermal wave field expression obtained in step S4-2 under the condition of steady heat loss is demonstrated, and it is confirmed that the heat loss coefficient has a great influence on the thermal wave amplitude.

[0075] Figure 4: is a surface diagram of the phase change at different heat loss coefficients and depths provided in this embodiment. In the figure, when the phase is at a fixed depth (length) position, the phase delay decreases as the heat loss coefficient increases. There is a negative correlation between the phase delay and the heat loss coefficient, and the rate of change of the phase delay decreases as the heat loss coefficient increases. Figure 4 It also demonstrates the validity of the thermal wave field expression obtained in step S4-2 under the condition of steady heat loss, and confirms that the heat loss coefficient has a certain influence on the phase delay.

[0076] Step S4-3: When the heat has been completely diffused into the entire sample and the temperature has reached a stable state, the spatial heat loss coefficient of the sample at the entire depth should be further considered. The spatial heat loss coefficient has a polynomial dependence on the depth x, and the spatial heat loss model expression is constructed:

[0077]

[0078] Among them, h n , (n=0,1,…,n) represents the heat loss coefficient along the side wall.

[0079] In this embodiment, the quadratic spatial heat loss model H(x)=H 0 +H 1 x+H 2 x 2 It is used to fit the experimental data optimally, where H n =h n +(W / B)h sn .

[0080] Step S4-4: Using the power series solution method, the spatial heat loss model expression is substituted into the one-dimensional thermal wave equation expression of the temperature response in the frequency domain obtained in step S4-2, and the generalized solution of the thermal wave field with spatial heat loss can be further obtained:

[0081]

[0082] Among them, a n is a system of algebraic equations with a set of solutions.

[0083] Step S4-5: Substitute the generalized solution of the thermal wave field of spatial heat loss in step S4-4 into the one-dimensional thermal wave equation expression of the temperature response in the frequency domain obtained in step S4-2, and perform algebraic operations to obtain an, (n = 0, 1, ..., n) and a 0 The associated algebraic equations lead to the thermal wave field solution T(x,ω) with spatially dependent heat loss coefficient and depth variation:

[0084]

[0085] Among them, the leakage heat wave number considering the space heat loss is defined as It changes with the heat loss value of the space.

[0086] Figure 5 It is a surface diagram of the change of logarithmic amplitude under the same heat loss coefficient and depth provided in this embodiment. In the figure, under the condition that the depth (length) of the logarithmic amplitude remains unchanged, the logarithmic amplitude increases with the increase of the heat loss coefficient, and the rate of change of the logarithmic amplitude decreases with the increase of the heat loss coefficient. Under the condition that the value of the heat loss coefficient remains unchanged, there is a nonlinear phenomenon in the logarithmic amplitude along the depth (length) direction, which proves that the distribution of the logarithmic amplitude along the depth (length) direction can be accurately controlled by controlling the heat loss in space.

[0087] Figure 6 It is a surface diagram of the phase change under the same heat loss coefficient and depth provided in this embodiment. In the figure, under the condition that the depth (length) of the phase remains unchanged, the phase increases with the increase of the heat loss coefficient, wherein the rate of change of the phase delay decreases with the increase of the heat loss coefficient. Similarly, with the change of the heat loss coefficient, the phase exhibits a nonlinear phenomenon, and this nonlinear phenomenon is controllable along the depth (length) direction.

[0088] Step S5: The collected experimental data at different modulation frequencies are processed by phase-locked digital technology to obtain the temperature response phase and amplitude information associated with the depth, and then obtain the phase temperature field data distribution results associated with the depth and the amplitude temperature field data distribution results associated with the depth.

[0089] Step S6: In this embodiment, for the temperature response phase and amplitude depth information data at five modulation frequencies, each modulation frequency includes four fitting parameters h s0 ,H 0 ,H 1 ,H 2 and a fixed material thermal diffusivity α, first obtain the thermal diffusivity α and the heat loss coefficient model parameter h in the Parteo frontier s0 ,H 0 ,H 1 ,H 2 The optimal value range is limited and the initialization parameters are set. The heat loss coefficient range is h s0 ,H 0 ,H 1 ,H 2 ∈[0,1×10 -6 ](W / m 2 .K), thermal diffusion coefficient α∈[0,10×10 -6 ](m 2 / s). Then, the multi-objective particle swarm optimization (MOPSO) algorithm is used to minimize the amplitude residual / phase residual under five modulation frequencies, and then the individual optimal fitting heat loss coefficient model parameters h under five modulation frequencies can be obtained. s0 ,H 0 ,H 1 ,H 2 and the individual best-fit thermal diffusivity α.

[0090] Step S7: Calculate the Parteo frontier based on the individual optimal fitting parameters under the five modulation frequencies to obtain the global optimal fitting heat loss coefficient model parameter h s0 ,H 0 ,H 1 ,H 2 and the global optimal fitting thermal diffusion coefficient α, determine the global optimal fitting heat loss coefficient model parameter h s0 ,H 0 ,H 1 ,H 2 The value satisfies the residual minimization convergence criterion. If so, the iteration ends and the global optimal fitting thermal diffusion coefficient α is output, thereby obtaining the thermal diffusivity of the sample. If not, the iteration continues and the heat loss coefficient model parameter h is further adjusted. s0 ,H 0 ,H 1 ,H 2 value, and continue with steps S6 and S7 until the iteration ends. In this embodiment, according to the global best fitting result, the thermal diffusion coefficient of the metal powder compacted sheet is α=7.20×10 -6 (m 2 / s), which is consistent with the existing result value of 3.75×10 -6 (m 2 / s) are in the same numerical range.

[0091] In summary, this embodiment confirms the effectiveness of the proposed spatial heat loss model and the depth-dependent thermal wave equation obtained by considering the spatial heat loss model. The thermal diffusivity of the metal powder compacted sheet obtained by this embodiment is in the same numerical range as the existing result value of the material, confirming the innovation and effectiveness of the thermal diffusivity measurement method considering spatial heat loss proposed by the present invention.

Claims

1. A method for measuring thermal diffusivity taking into account spatial heat loss, characterized in that The following steps are involved: Step S1: Keep the front surface of the sample to be tested and the light source emitter at the same horizontal position, so that the modulated thermal light source emitted by the light source emitter can completely irradiate the front surface of the sample to be tested; Step S2: setting modulation signals with different modulation frequencies through the light source transmitter, and applying thermal excitations with different modulation frequencies to the front surface of the sample to be tested, so that the sample generates heat diffusion inside and heat radiation on the surface; Step S3: using an infrared thermal imager to collect and record temperature field data of the upper surface of the sample specimen at different modulation frequencies; Step S4: establishing a thermal model; Step S5: extracting the temperature response phase and amplitude depth information to obtain the temperature field data distribution results that vary with depth at different modulation frequencies; Step S6: fitting the temperature field data distribution results under different modulation frequencies to obtain the individual best fitting heat loss coefficient model parameters and the individual best fitting thermal diffusion coefficient under different modulation frequencies; Step S7: According to the individual optimal fitting parameters under different modulation frequencies, the global optimal fitting heat loss coefficient model parameters and the global optimal fitting thermal diffusion coefficient are obtained, and it is determined whether the global optimal fitting heat loss coefficient model parameter value meets the residual minimization convergence criterion. If it does, the iteration ends, and the global optimal fitting thermal diffusion coefficient is output to obtain the thermal diffusivity of the sample; if it does not meet the criterion, the iteration continues, and the heat loss coefficient model parameter value is further adjusted, and steps S6 and S7 are continued until the iteration ends.

2. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 1, characterized in that In step S1, based on the phase-locked thermal imaging measurement method, the sample is fixed on the experimental table, the sample is stable and does not move, and an appropriate distance is maintained between the sample specimen and the thermal excitation source and the infrared thermal imager to ensure the uniformity of the thermal excitation and the field of view of the infrared thermal imager covers the sample surface.

3. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 1, characterized in that The window-shaped opening is arranged so that after the thermal excitation source passes through the window-shaped opening, it can only irradiate and cover the front surface of the sample specimen, and the other surfaces of the specimen are not irradiated by the heat source.

4. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 1, characterized in that In step S2, periodic thermal excitation is applied to the surface of the sample specimen, and the frequency and power of the thermal excitation source are adjusted to allow the thermal excitation to propagate in the sample specimen.

5. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 1, characterized in that The step S4 specifically comprises the following steps: Step S4-1: taking into account the steady-state heat loss of the sample specimen material, a new two-dimensional thermal wave equation that changes with time is established; Step S4-2: transforming the obtained two-dimensional space heat wave equation into the time-frequency domain to obtain a one-dimensional space heat wave equation in the frequency domain, and then obtaining a steady heat flux equation under steady heat loss conditions; Step S4-3: construct a spatial heat loss model. The spatial heat loss and the depth of the sample specimen are interdependent, and the thermal diffusion coefficient decreases with increasing depth; Step S4-4: using a power series solution method to obtain a generalized solution of the thermal wave field with spatial heat loss; Step S4-5: By comparing and determining the expressions of the generalized solution coefficients, a thermal wave field solution with spatially dependent heat loss coefficients and depth variations is obtained.

6. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 5, characterized in that The constant heat loss expression in step S4-1 is constructed as follows: Among them, h represents the heat loss coefficient of the material, k represents the thermal conductivity of the material, C represents the geometric perimeter of the front surface of the sample C=2(B+W), S represents the area of ​​the front surface of the sample S=BW, B represents the height of the sample, and W represents the width of the sample.

7. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 5, characterized in that In step S4-2, the two-dimensional space heat wave equation obtained in step S4-1 is still a function related to temperature and time. A Fourier transform is performed on it to obtain a frequency domain expression under temperature response, and then a steady heat flux expression T(x,ω) under steady heat loss conditions is obtained: Among them, on the front surface of the sample specimen, the flux generated by the modulated light beam is defined as q0; the heat loss coefficient of the front surface is hs0; the leakage heat wave number is defined as σ represents the attenuation constant of the thermal wave, m represents the thermal wave number, and σ 2 +m 2 represents the overall characteristics of the heat wave when it propagates in the material, ei ω t represents the corresponding relationship of temperature transformation from time domain to frequency domain.

8. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 5, characterized in that In step S4-3, when the temperature of the specimen reaches a stable state, the spatial heat loss coefficient of the sample specimen at the entire depth is further considered. The spatial heat loss coefficient and the depth x have a polynomial dependency relationship, and the spatial heat loss model expression is constructed: Where hn, (n=0,1,…,n) represents the heat loss coefficient along the side wall.

9. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 5, characterized in that In step S4-4, a power series solution method is used to bring the spatial heat loss model expression into the frequency domain expression under the temperature response obtained in step S4-2, and further obtain a generalized solution of the thermal wave field with spatial heat loss: Among them, a n is a system of algebraic equations with a set of solutions.

10. A method for measuring thermal diffusivity taking into account spatial heat loss according to claim 5, characterized in that In step S4-5, the generalized solution of the thermal wave field of spatial heat loss in step S4-4 is brought into the frequency domain expression under the temperature response obtained in step S4-2, thereby obtaining the final thermal wave field solution T(x,ω) of the spatial heat loss coefficient varying with depth: Among them, the leakage heat wave number considering the space heat loss is defined as It changes with the heat loss value of the space.

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