Thermal physical parameter full-automatic measurement method and system

Abstract

The invention belongs to the field of material science, and discloses a thermophysical parameter full-automatic measurement method and system, and the method comprises the steps: collecting pulse voltage signal waveform data U applied to a plane heat source, loop current signal waveform data I, and thermocouple voltage waveform data representing the temperature of a sample; the collected data are preprocessed; and obtaining thermophysical parameters of the sample based on the preprocessed data. The thermal conductivity and the thermal diffusion coefficient of the mineral rock under various temperature conditions can be efficiently and accurately measured.

Description

Technical Field

[0001] The present invention is a method for measuring the thermal conductivity and thermal diffusivity of mineral rocks under various temperature conditions. It belongs to the field of materials science and is used to evaluate the heat transfer performance of materials. Specifically, it relates to a fully automatic measurement method and system for thermophysical parameters. Background Art

[0002] The traditional method for measuring the thermal conductivity and thermal diffusivity of mineral rocks under high temperature and high pressure is to manually apply heating pulses through a pulse circuit, record the heating pulse data and sample temperature change data with an oscilloscope, and then calculate and fit the thermal conductivity and thermal diffusivity after manual processing. This method has the following shortcomings:

[0003] 1. Human intervention is frequent. Traditional test data is recorded by an oscilloscope, which has no targeted filtering function. Microvolt-level thermoelectric electromotive force signals are easily interfered with, and manual data screening is often required, which is prone to errors.

[0004] 2. The pulse power value is inaccurate. Pulse power is an important parameter for calculating thermal conductivity. The resistance value of the heater will change in different temperature environments. The traditional method is to replace the resistance value at high temperature with the resistance value measured at room temperature, or to calculate the resistance value based on the typical resistivity value of the heating material, which has a large error. According to the formula: P = I 2 R, resulting in errors in the power value.

[0005] 3. The measured data needs to be imported into other numerical analysis or drawing software for fitting and drawing graphs, which is inefficient. Summary of the Invention

[0006] In order to solve the problems existing in the prior art, the present invention provides a fully automatic measurement method and system for thermophysical parameters, which can efficiently and accurately measure the thermal conductivity and thermal diffusivity of mineral rocks under various temperature conditions.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A fully automatic measurement method for thermophysical parameters, the method comprising:

[0009] Collecting pulse voltage signal waveform data U applied to the planar heat source, loop current signal waveform data I, and thermocouple voltage waveform data representing the sample temperature;

[0010] Preprocess the collected data;

[0011] Based on the preprocessed data, the thermophysical parameters of the sample are obtained.

[0012] Preferably, the method for preprocessing the collected data includes:

[0013] Construct a method to automatically determine the lowest frequency of noise and the number of wavelet decomposition layers for denoising;

[0014] Convert thermocouple signals to actual temperatures.

[0015] Preferably, a method for constructing a method for automatically determining the lowest frequency of noise and the number of wavelet decomposition layers for denoising includes:

[0016] Based on LabVIEW's FFT power spectrum and PSDVI, the autopower spectrum of temperature data is obtained;

[0017] Smoothing of autopower spectrum data;

[0018] The first-order derivative of the smoothed data is calculated to obtain the rate of change of the autopower spectrum with frequency;

[0019] The minimum frequency value corresponding to the change rate tending to 0 at two consecutive points is the lowest noise frequency f N ;

[0020] Decomposition level N: 2 N <f s / (2f N ), N takes the maximum value, f s is the sampling frequency;

[0021] The wavelet transform is performed based on the WA1D Discrete Wavelet Transform VI in LabVIEW. The number of decomposition levels is set to the desired value N, and the wavelet basis is selected as sym8 to obtain the wavelet coefficients.

[0022] Keep the approximate coefficients and set all the detail coefficients to 0 to obtain a new set of wavelet coefficients;

[0023] The inverse wavelet transform is performed with the new wavelet coefficients to obtain the filtered temperature signal.

[0024] Preferably, the method for obtaining the thermophysical parameters of the sample based on the preprocessed data includes:

[0025] Determine and calculate the actual pulse width τ;

[0026] Calculate the reference temperature, i.e., the background temperature, and the temperature change ΔT;

[0027] The values ​​of fitting parameters A and B;

[0028] Calculate pulse power;

[0029] Solve for thermal conductivity, thermal diffusivity, and specific heat capacity.

[0030] Preferably, the method of determining and calculating the actual pulse width τ, calculating the reference temperature (ie, background temperature), and the temperature change ΔT includes:

[0031] Through the waveform data of pulse voltage and loop current, the amplitude of each sampling point is directly read, that is, the pulse voltage U and loop current I at each sampling moment. According to the measurement principle, the actual pulse width τ and temperature change ΔT are calculated;

[0032] The measurement principle is: Under a certain stable temperature and pressure environment, a thermal disturbance is given to the sample, and the temperature change ΔT is expressed as:

[0033] Where t>τ(1)

[0034] Where n is the summation range. The larger the value of n, the higher the calculation accuracy. The value of n is determined according to the actual accuracy requirements. x represents the distance between the plane heat source and the thermocouple, and d represents the thickness of the sample in the heat conduction direction. Parameters A and B are defined as:

[0035]

[0036] Where P represents the pulse heating power, S represents the area of ​​the planar heat source, D represents the thermal diffusivity, and κ represents the thermal conductivity;

[0037] Methods for fitting the values ​​of parameters A and B include:

[0038] According to formula (1), the actual measured heating pulse width value τ is substituted. Based on the nonlinear fitting VI of LabVIEW, the formula after the substitution value is used as the formula model, the weight is set to 1, the initial values ​​of A and B are set to 1, the relative time t relative to the pulse trigger moment is used as the independent variable, and the ΔT value corresponding to time t is used as the dependent variable. Fitting is performed, and the best fitting parameters output by the VI are the values ​​of A and B.

[0039] Preferably, the method for calculating the pulse power includes: constructing a method for calculating the pulse power by measuring the pulse voltage U and the loop current I in real time;

[0040] When calculating the effective pulse power, the planar heat source is assumed to be a purely resistive load, then:

[0041]

[0042] P 总 Calculated by solving the integral method, the calculation formula is:

[0043]

[0044] Combining (3) and (4), we can get P 热源 The calculation formula is as follows:

[0045]

[0046] Among them, R 线缆 In the pulse heating circuit, except for the plane heat source resistance R 热源 The sum of all resistances other than 总 is the total resistance of the pulse heating circuit.

[0047] Preferably, R 总 The measurement methods include:

[0048] The pulse voltage waveform curve is used to measure the end time of the pulse rising edge and the start time of the pulse falling edge. The pulse voltage U waveform and the loop current I waveform between the two moments are intercepted. According to R=U / I, the R of the intermediate period is obtained. 总 curve;

[0049] Extract R 总 The 20 sampling points before and after the curve are used as the reference points for the resistance change trend;

[0050] Based on the LabVIEW Generalized Polynomial VI, polynomial fitting is performed on each of the 20 sampling points. The polynomial order is set to 3, the fitting method is set to least squares, and the tolerance is set to 1E-6. This results in the third-order polynomial formulas for the front and back resistance curves, i.e., the curve formulas for the resistance change over time in the rising and falling segments.

[0051] Substitute the sampling time value of the rising edge segment into the front-segment formula to calculate the resistance value of the front segment, and substitute the sampling time value of the falling edge segment into the back-segment formula to calculate the resistance value of the back segment;

[0052] Connect the front, middle and rear resistance curves to get the complete R 总 Change curve.

[0053] Preferably, the method for solving thermal conductivity, thermal diffusivity and specific heat capacity includes:

[0054] According to the calculation formula of thermal conductivity κ and thermal diffusion coefficient D given in the measurement principle, thermal conductivity κ and thermal diffusion coefficient D are obtained and substituted into formula (6) to solve the specific heat capacity;

[0055]

[0056] Where ρ is the sample density.

[0057] The present invention also provides a fully automatic measurement system for thermophysical parameters, which is used to implement the above method. The system includes: an acquisition module, a preprocessing module, and a calculation module;

[0058] The acquisition module is used to acquire the pulse voltage signal waveform data U applied to the planar heat source, the loop current signal waveform data I, and the thermocouple voltage waveform data representing the sample temperature;

[0059] The preprocessing module is used to preprocess the collected data;

[0060] The calculation module is used to obtain the thermophysical parameters of the sample based on the preprocessed data.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention provides a fully automatic measurement method and system for thermophysical parameters, which avoids human intervention in the data acquisition and processing process, and reliably guarantees data accuracy through multiple sampling and averaging, alternating application of positive and negative polarity pulses, and digital filtering of temperature data.

[0063] The present invention can complete a measurement and obtain the final result within a few minutes, which is less than one-tenth of the time taken by traditional methods.

[0064] The present invention provides complete data visualization function, which facilitates testers to make fine adjustments to various parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 This is a schematic diagram of the control structure of an embodiment of the present invention;

[0067] Figure 2 This is a diagram of the software running interface of an embodiment of the present invention;

[0068] Figure 3 This is the power spectrum of temperature data according to an embodiment of the present invention;

[0069] Figure 4 This is a comparison chart of temperature data before and after filtering according to an embodiment of the present invention;

[0070] Figure 5 This is a comparison diagram of the planar heat source resistor before and after treatment according to an embodiment of the present invention;

[0071] Figure 6 This is a measurement result report diagram of an embodiment of the present invention;

[0072] Figure 7 Creating a flow chart for a data file according to an embodiment of the present invention;

[0073] Figure 8 This is a data collection flow chart for an embodiment of the present invention;

[0074] Figure 9 This is a flow chart of converting thermocouple voltage data into temperature data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0076] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Example 1

[0078] The present invention is a method for measuring the thermal conductivity and thermal diffusivity of mineral rocks under various temperature conditions. The method is applicable to the field of materials science and is used to evaluate the heat transfer performance of materials.

[0079] The present invention develops a set of methods that integrate all processes (including data acquisition, preprocessing, numerical calculation and fitting, data storage and graphics) to efficiently and accurately measure the thermal conductivity and thermal diffusivity of mineral rocks under various temperature conditions.

[0080] The software is written in LabVIEW (32-bit) and controls the data acquisition card through the USB3.0 interface to send heating pulses and collect data. After data processing, it is stored in a data file and the data waveform is displayed synchronously (see the control structure for details). Figure 1 ).

[0081] The present invention provides a fully automatic measurement method for thermophysical parameters, the method comprising:

[0082] Collecting pulse voltage signal waveform data U applied to the planar heat source, loop current signal waveform data I, and thermocouple voltage waveform data representing the sample temperature;

[0083] Preprocess the collected data;

[0084] Based on the preprocessed data, the thermophysical parameters of the sample are obtained.

[0085] In this embodiment, ① data acquisition utilizes high-precision software timing to precisely control the heating pulse width and the timing of each process. During each acquisition, a trigger signal is sent from the acquisition card's digital output channel to control the on / off switching of the MOSFET, thereby applying a heating pulse to the planar heat source. Simultaneously, the acquisition card uses its analog input channel to capture the pulse voltage signal waveform data (U), the loop current signal waveform data (I), and the thermocouple voltage waveform data representing the sample temperature.

[0086] ② The method of collecting data multiple times to calculate the average value can reduce the accidental error that may occur in a single measurement. The number of collection times is set by parameters.

[0087] ③ In addition, the rising or falling edge of the heating pulse will generate an induced electromotive force at the thermocouple, affecting the measurement accuracy of the thermocouple. This method uses alternating application of positive and reverse polarity heating pulses to offset this induced electromotive force and weaken its impact on the thermocouple signal.

[0088] In this embodiment, data preprocessing includes:

[0089] ① Denoising: The rapid voltage changes on the rising and falling edges of the heating pulse inevitably cause electromagnetic interference to the thermocouple signal. Furthermore, because the thermocouple signal is very weak, varying in the microvolt range, it can easily be interfered with by high-frequency electromagnetic waves in the measurement environment. Therefore, after data acquisition is complete, software digital filtering is performed.

[0090] ②Convert the thermocouple signal into actual temperature.

[0091] Specifically, a method for automatically determining the lowest frequency of noise and the number of wavelet decomposition layers for denoising includes:

[0092] Based on LabVIEW's FFT power spectrum and PSDVI, the autopower spectrum of temperature data is obtained;

[0093] Smoothing of autopower spectrum data;

[0094] The first-order derivative of the smoothed data is calculated to obtain the rate of change of the autopower spectrum with frequency;

[0095] The minimum frequency value corresponding to the change rate tending to 0 at two consecutive points is the lowest noise frequency f N ;

[0096] Decomposition level N: 2 N <f s / (2f N ), N takes the maximum value, f s is the sampling frequency;

[0097] The wavelet transform is performed based on the WA1D Discrete Wavelet Transform VI in LabVIEW. The number of decomposition levels is set to the desired value N, and the wavelet basis is selected as sym8 to obtain the wavelet coefficients.

[0098] Keep the approximate coefficients and set all the detail coefficients to 0 to obtain a new set of wavelet coefficients;

[0099] The inverse wavelet transform is performed with the new wavelet coefficients to obtain the filtered temperature signal.

[0100] In this embodiment, the method for obtaining the thermophysical parameters of the sample (i.e., numerical calculation and fitting) based on the preprocessed data includes:

[0101] Determine and calculate the actual pulse width τ;

[0102] Calculate the reference temperature, i.e., the background temperature, and the temperature change ΔT;

[0103] The values ​​of fitting parameters A and B;

[0104] Calculate pulse power;

[0105] Solve for thermal conductivity, thermal diffusivity, and specific heat capacity.

[0106] In this embodiment, the method of determining and calculating the actual pulse width τ, calculating the reference temperature (ie, background temperature), and the temperature change ΔT includes:

[0107] Through the waveform data of pulse voltage and loop current, the amplitude of each sampling point is directly read, that is, the pulse voltage U and loop current I at each sampling moment. According to the measurement principle, the actual pulse width τ and temperature change ΔT are calculated;

[0108] The measurement principle is: Under a certain stable temperature and pressure environment, a thermal disturbance is given to the sample, and the temperature change ΔT is expressed as:

[0109] Where t>τ(1)

[0110] Where n is the summation range. The larger the value of n, the higher the calculation accuracy. The value of n is determined according to the actual accuracy requirements. x represents the distance between the plane heat source and the thermocouple, d represents the thickness of the sample in the heat conduction direction, and the units of length and time are meters (m) and seconds (s), respectively. Parameters A and B are defined as:

[0111]

[0112] Where P represents the pulse heating power (W), S represents the area of ​​the plane heat source (m 2 ), D represents the thermal diffusivity (mm 2 / s), and κ represents thermal conductivity (W / mK). When n is set to 15, sufficiently accurate experimental results can be obtained. After the sample is prepared, x, d, and S are fixed and can be set as fixed parameters. The only variables are τ (actual pulse width) and P (pulse power). The power is related to the applied voltage U and current I. Therefore, only τ, U, I, and ΔT need to be measured. ΔT can be fitted using formula (1) to calculate the values ​​of A and B, and then substituted into formula (2) to obtain the thermal conductivity and thermal diffusivity of the sample.

[0113] Methods for fitting the values ​​of parameters A and B include:

[0114] According to formula (1), the actual measured heating pulse width value τ is substituted. Based on the nonlinear fitting VI of LabVIEW, the formula after the substitution value is used as the formula model, the weight is set to 1, the initial values ​​of A and B are set to 1, the relative time t relative to the pulse trigger moment is used as the independent variable, and the ΔT value corresponding to time t is used as the dependent variable. Fitting is performed, and the best fitting parameters output by the VI are the values ​​of A and B.

[0115] In this embodiment, the method for calculating pulse power includes: constructing a method for calculating pulse power by measuring the pulse voltage U and the loop current I in real time;

[0116] When calculating the effective pulse power, the planar heat source is assumed to be a purely resistive load, then:

[0117]

[0118] P 总 Calculated by solving the integral method, the calculation formula is:

[0119]

[0120] Combining (3) and (4), we can get P 热源 The calculation formula is as follows:

[0121]

[0122] Among them, R 线缆 In the pulse heating circuit, except for the plane heat source resistance R 热源 The sum of all resistances other than 总 is the total resistance of the pulse heating circuit.

[0123] In this embodiment, R 总 The calculation is achieved by the following method:

[0124] The pulse voltage waveform curve can accurately measure the end time of the pulse rising edge and the start time of the pulse falling edge, intercept the pulse voltage U waveform and loop current I waveform between these two moments, and obtain R in the middle period according to R=U / I 总 curve;

[0125] Extract 20 sampling points before and after the above resistance curve as the reference points of the resistance change trend;

[0126] Based on the LabVIEW generalized polynomial VI, polynomial fitting is performed on the above 20 points, where the polynomial order is set to 3, the fitting method is set to the least squares method, and the tolerance is set to 1E-6. The third-order polynomial formulas of the front and rear resistance curves are obtained, that is, the curve formulas of the resistance change over time in the rising and falling segments; where the formula is in the form of Y=aX 3 +bX 2 The third-order polynomial of +cX+d, the coefficients a, b, c and d of the formula are obtained through fitting. Different planar heat sources, different ambient temperatures, and different heating pulses have different coefficients of the formula. Fitting is performed every time a measurement is made to obtain the formula.

[0127] Substitute the sampling time value of the rising edge segment into the front-segment formula to calculate the resistance value of the front segment, and substitute the sampling time value of the falling edge segment into the back-segment formula to calculate the resistance value of the back segment;

[0128] Connect the front, middle and rear resistance curves to get the complete R 总 Change curve.

[0129] In this embodiment, the method for calculating thermal conductivity, thermal diffusivity, and specific heat capacity includes:

[0130] According to the calculation formula of thermal conductivity κ and thermal diffusion coefficient D given in the measurement principle, thermal conductivity κ and thermal diffusion coefficient D are obtained and substituted into formula (6) to solve the specific heat capacity;

[0131]

[0132] Where ρ is the sample density.

[0133] In this embodiment, ① data storage: all data of a measurement is stored in a file as a data table to facilitate subsequent call and review.

[0134] ②Graphic drawing: Draw the waveform curve of each signal and realize the display / hiding, zooming and panning functions of the curve.

[0135] Example 2

[0136] Take the olivine thermophysical parameter test experiment under 1GPa pressure as an example: the software operation interface is shown in Figure 2

[0137] Step 1. Experimental parameter settings: Click "Parameter Settings", set the following parameters in the pop-up window, and click "Save".

[0138] Sampling parameters:

[0139] Sampling rate (1K~250K), the number of signal points collected by the acquisition card per second. Set to: 100000;

[0140] Sampling period (seconds), the time interval between two adjacent samplings (the start time of this sampling - the start time of the previous sampling), is set to: 2;

[0141] Sampling time (seconds), the length of time for each sampling signal, set to: 0.2;

[0142] Pulse delay (seconds), the time interval from the start of sampling to the triggering of the heating pulse, the minimum value is 0.05, set to: 0.05;

[0143] Pulse width (milliseconds), the duration of the heating pulse, is set to: 3;

[0144] Pulse form, polarity selection of heating pulse, set to alternating positive and negative;

[0145] Sampling times, the total number of times data collection is performed, is set to: 128;

[0146] Pulse detection threshold (V), the voltage threshold for detecting whether a heating pulse is triggered, is set to: 0.1.

[0147] Measurement parameters:

[0148] Reference Levels: High Ref Level specifies the high reference level of the waveform in percentage or absolute units (default). After a signal crosses the mid ref level, it must cross the high ref level before the next falling mid ref level crossing is counted. The high ref level is set to 0.3. Mid Ref Level specifies the middle reference level of the waveform in percentage or absolute units (default). The pulse width is the time difference between the first two mid ref level crossings of the pulse, measured in seconds. Every mid ref level crossing must include at least one high or low ref level crossing. The mid ref level is set to 0.2. Low Ref Level specifies the low reference level of the waveform in percentage or absolute units (default). After a signal crosses the mid ref level, it must cross the low ref level before the next rising mid ref level crossing is counted. The low ref level is set to 0.1. Reference Units specifies whether the high, mid, and low ref levels are entered as percentages (of the entire waveform) or absolute levels. By default, they are in absolute levels. Reference Units is set to Absolute (V).

[0149] Fitting parameters:

[0150] Fitting value method: Mouse, "Mouse" method: click the point on the "Sample Temperature" curve with the mouse as the starting point (end point) of the fitting value. "Parameter" method: use the time value set by "Fitting start point" ("Fitting end point") as the fitting start point (end point);

[0151] Fitting start point (s) 0.0031, the abscissa (time axis) of the starting point of the curve segment on the "Sample Temperature" curve that you want to fit;

[0152] Fitting end point (s) 0.02, the abscissa (time axis) of the end point of the curve segment on the “sample temperature” curve that you want to fit;

[0153] Sample thickness (mm): 0.62, the size of the sample in the direction of temperature conduction;

[0154] Sample area (mm 2 )7.069, the cross-sectional area of ​​the sample perpendicular to the direction of temperature conduction;

[0155] Wire resistance (Ω) 2.34, the sum of all resistances in the entire measurement circuit except the sample resistance;

[0156] Sample density (kg / m^3) 3355, average density of the sample;

[0157] The temperature data comes from the original temperature. Select the temperature data used for fitting.

[0158] Temperature parameters:

[0159] Cold junction temperature (°C) 18, ambient temperature of the thermocouple cold junction;

[0160] Temperature unit Kelvin (K), temperature unit, Celsius / Fahrenheit / Kelvin respectively;

[0161] Thermocouple type K, thermocouple calibration number.

[0162] File parameters:

[0163] Image size: width 590, height 300, the size of the waveform image file saved in the Excel data file;

[0164] The sampling data saving path is E:\Thermophysical Parameter Measurement\Data\1GPa.tdms. The file name should contain the suffix "tdms", for example: E:\test.tdms. When the file is stored, two files are generated in the same folder, one is the setting file, and the other is the report file. The report file name is "data file name + report xlsx", for example: E:\test report xlsx;

[0165] Sample name KW295-Olivine. This parameter is only used in report files and does not need to be set.

[0166] Tester YXX This parameter is only used in report files and does not need to be set.

[0167] Step 2: Data collection:

[0168] ① Click "Data Collection" to first determine whether the data files have duplicate names. See the process for details. Figure 7 :

[0169] ②Automatically enter data collection, see the process Figure 8 The running status is displayed as "Data Collection" (flashing), and the current sampling times are also displayed.

[0170] ③ Implementation of High-Precision Timing: Windows, as a non-real-time operating system, only provides a timing accuracy of 1ms, which is far from meeting the requirements of this measurement method. We call the Windows dynamic link library kernel32.dll, read the system clock frequency (function name QueryPerformanceFrequency) as the timing unit, and the corresponding counter value (function name QueryPerformanceCounter) change as the timing time. We encapsulate this as a subVI, set the highest priority, and run it in a separate loop. When the program executes pulse delay and timing pulse, it calls this subVI. Simulation tests show that the timing accuracy is <2μs, with a typical value of 0.5μs, which meets the timing accuracy requirements of this measurement method.

[0171] Step 3: Data preprocessing: After data collection is completed, the data will automatically enter the preprocessing process.

[0172] ① The collected temperature data will inevitably have noise interference. In the usual thermal conductivity measurement, the original temperature data is used for subsequent fitting calculations. However, due to the existence of noise interference, the original temperature data fluctuates in a relatively wide range. Therefore, the subsequent fitting will cause large errors due to different point selections. In the data preprocessing stage, we filter the original temperature data through wavelet transform to remove noise interference in order to obtain accurate temperature data. How to determine the noise frequency band and then determine the number of wavelet decomposition layers and wavelet coefficients is the key. By analyzing the autopower spectrum of the temperature signal (see Figure 3 ), the following conclusions are drawn:

[0173] A. The effective temperature signal is a low-frequency signal;

[0174] B. The effective signal power decays rapidly as the frequency increases;

[0175] C. The power of noise interference is very small;

[0176] D. The power of noise interference changes smoothly;

[0177] E. Noise interference is in the high frequency band relative to the effective signal.

[0178] Based on the above conclusions, we constructed a method to automatically determine the lowest frequency of noise and the number of wavelet decomposition layers:

[0179] A. Call LabVIEW's FFT power spectrum and PSDVI to obtain the autopower spectrum of the temperature data;

[0180] B. Smoothing the autopower spectrum data (performing a median filter with a rank of 10);

[0181] C. Take the first-order derivative of the smoothed data to obtain the rate of change of the autopower spectrum with frequency;

[0182] D. The minimum frequency value corresponding to the change rate tending to 0 at two consecutive points (range ±0.05) is the lowest noise frequency f N ;

[0183] E. Decomposition level N: 2 N <f s / (2f N ), N takes the maximum value (f s is the sampling frequency);

[0184] F. Call the WA1D Discrete Wavelet Transform VI in LabVIEW to perform the wavelet transform. Set the number of decomposition levels to the value N calculated in the previous step and select sym8 as the wavelet basis to obtain the wavelet coefficients.

[0185] G. Keep the approximate coefficients and set all the detail coefficients to 0 to obtain a new set of wavelet coefficients;

[0186] H. Perform inverse wavelet transform with the new wavelet coefficients to obtain the filtered temperature signal. (See the attached temperature waveform comparison before and after denoising. Figure 4 ).

[0187] ② According to the preset thermocouple model, use the temperature division function and inverse function formula given in the national standard GBT16839.1-2018 (Thermocouple Part 1: Electromotive Force and Tolerance) to calculate the actual temperature. Figure 9 :

[0188] Step 4. Store Raw Data: Based on the parameterized sampling data file path and name, store the waveform data for thermocouple voltage, raw temperature, filtered temperature, pulse voltage, and loop current, along with the sampling rate, cable resistance, sample thickness, sample area, pulse threshold, and sampling times, into a sample data file. The data file uses the TDMS format, which allows for near-real-time storage and reading, significantly improving efficiency for large data volumes.

[0189] Step 5. Sampled Data Display: Use LabVIEW's waveform graph control to automatically plot waveform data. The waveform graph control's graphical tool palette allows you to zoom in on rectangular areas, horizontal areas, vertical areas, display a full view, zoom in by the cursor position, and zoom out by the cursor position. You can also zoom in and out of the waveform graph by clicking and modifying the axis scale values. The waveform graph legend allows you to display or hide waveform curves.

[0190] Step 6. Numerical calculation and fitting: Click "Data Analysis" and select the fitting start and end points on the temperature curve. The fitting interval of this experiment is: 0.003489s~0.01377s.

[0191] The amplitude of each sampling point, i.e., the pulse voltage U and loop current I at each sampling moment, can be directly read from the waveform data of the pulse voltage and loop current. Based on the aforementioned measurement principle, the actual pulse width τ and temperature change ΔT also need to be calculated.

[0192] ① τ (actual pulse width) measurement: By setting the high, medium, and low reference levels, the starting time of the pulse rising edge and the ending time of the falling edge are determined. The actual pulse width = the ending time of the falling edge - the starting time of the rising edge.

[0193] Starting time of rising edge: Detect the heating pulse waveform. When the pulse voltage is higher than the middle reference level and continues to upload the high reference level, it is considered to be the rising edge of the pulse. The time axis (horizontal axis) coordinate of the intersection of the middle reference level and the heating pulse waveform is the starting time of the rising edge.

[0194] Falling edge end time: Similar to the rising edge start time, the judgment is based on whether the pulse voltage continues to cross the low reference level after falling below the middle reference level.

[0195] ②ΔT calculation: First calculate the reference temperature (background temperature). The average temperature data between the start of data acquisition and the triggering of the heating pulse is used as the reference temperature. ΔT = measured temperature - reference temperature.

[0196] (where t > τ), substitute the actual measured heating pulse width τ. Call the LabVIEW Nonlinear Fit VI, use the formula after substituting the values ​​as the formula model, set the weight to 1, set the initial values ​​of A and B to 1, use the relative time t relative to the pulse trigger moment as the independent variable, and the ΔT value corresponding to time t as the dependent variable. Perform the fit, and the best-fit parameters output by the VI are the values ​​of A and B.

[0197] ④P 热源 Calculation of (pulse power): In conventional thermal conductivity measurements, the pulse power calculation formula is:

[0198] P 热源 =I 2 R 热源 , where R 热源 Calculated by the typical resistivity value of the plane heat source material at a certain ambient temperature;

[0199] In actual measurement, when the heating pulse is applied, R 热源 The resistivity of the planar heat source varies with the surface temperature. Furthermore, the purity of the planar heat source material also affects the resistivity. This deviation in the resistance value can lead to inaccurate power calculations. We developed a method for calculating pulse power by measuring the pulse voltage U and loop current I in real time.

[0200] When calculating the effective pulse power, the planar heat source can be considered as a purely resistive load, and then:

[0201]

[0202] P 总 It can be accurately calculated by solving the integral method, and the formula is as follows:

[0203]

[0204] Combining (3) and (4), we can get P 热源 The calculation formula is as follows:

[0205]

[0206] R 线缆 In the pulse heating circuit, except for the plane heat source resistance R 热源 The sum of other resistances except for R is thermally insulated from the sample and can be approximately considered to be in a room temperature environment. The resistance fluctuation caused by room temperature fluctuation is extremely small and can be ignored. 线缆 It can be determined in advance and set as a fixed parameter.

[0207] R 总is the total resistance of the pulse heating circuit. According to Ohm's law, R = U / I, the resistance waveform curve can be obtained. However, in actual hardware circuits, the MOSFET in the pulse switching circuit has parasitic capacitance. During the rising and falling edges of the pulse, the parasitic capacitance charges and discharges. During this period, the measured pulse voltage U and the loop current I are out of phase. Therefore, the resistance calculated during the rising and falling time periods is inaccurate. We use a method to fit the front and back segments of the resistance change curve to estimate the resistance of the planar heat source during the rising and falling edges.

[0208] A. The pulse voltage waveform curve can accurately measure the end time of the pulse rising edge and the start time of the pulse falling edge. The pulse voltage U waveform and loop current I waveform between these two moments can be intercepted. According to R=U / I, R in the middle period can be obtained. 总 curve;

[0209] B. Extract 20 sampling points before and after the above resistance curve as the reference points of the resistance change trend;

[0210] C. Using the LabVIEW Generalized Polynomial VI, perform polynomial fits on each of the 20 points. Set the polynomial order to 3, the fitting method to least squares, and the tolerance to 1E-6. Obtain third-order polynomial formulas for the front and back resistance curves, i.e., the curve formulas for the resistance change over time for the rising and falling segments.

[0211] D. Substitute the sampling time value of the rising edge segment into the previous formula to calculate the resistance value of the first segment, and substitute the sampling time value of the falling edge segment into the next formula to calculate the resistance value of the next segment;

[0212] E. Connect the front, middle and rear resistance curves to get the complete R 总 Change curve (Comparison of resistance curve before treatment and resistance curve after treatment is shown in Figure 5 ).

[0213] The collected pulse voltage value U, loop current value I, actual pulse width τ, fixed parameter R 线缆 Substituting R into (5) can calculate the pulse power.

[0214] ⑤ Calculation of Thermal Conductivity, Thermal Diffusivity, and Specific Heat: The calculation formulas for thermal conductivity κ and thermal diffusivity D are given in the aforementioned measurement principle and will not be repeated here. Given κ and D, the specific heat can be calculated by substituting them into the following formula.

[0215]

[0216] Where ρ is the sample density (kg / m 3 , determined in advance).

[0217] Step 7: Automatically display the result data and generate a measurement report in Excel format (see Figure 6 ).

[0218] Experimental results: A = 25.3

[0219] B=50.2

[0220] R-square = 0.985 (the goodness of fit is close to 1, indicating that the fitting curve is highly consistent with the experimental data.)

[0221] κ: 5.35W / (m·K)

[0222] D: 1.95mm 2 / s

[0223] c p :816J / (kg·K)

[0224] The results are shown in Table 1:

[0225] Table 1

[0226]

[0227] Example 3

[0228] The present invention also provides a fully automatic measurement system for thermophysical parameters, which is used to implement the above method. The system includes: an acquisition module, a preprocessing module, and a calculation module;

[0229] The acquisition module is used to collect the pulse voltage signal waveform data U applied to the planar heat source, the loop current signal waveform data I, and the thermocouple voltage waveform data representing the sample temperature;

[0230] The preprocessing module is used to preprocess the collected data;

[0231] The calculation module is used to obtain the thermophysical parameters of the sample based on the preprocessed data.

[0232] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A fully automatic measurement method for thermophysical parameters, characterized in that: The method comprises: Collecting pulse voltage signal waveform data U applied to the planar heat source, loop current signal waveform data I, and thermocouple voltage waveform data representing the sample temperature; Preprocess the collected data; Based on the preprocessed data, the thermophysical parameters of the sample are obtained.

2. The method according to claim 1, characterized in that Methods for preprocessing the collected data include: Construct a method to automatically determine the lowest frequency of noise and the number of wavelet decomposition layers for denoising; Convert thermocouple signals to actual temperatures.

3. The method according to claim 2, characterized in that The method of constructing a method to automatically determine the lowest frequency of noise and the number of wavelet decomposition layers for denoising includes: Based on LabVIEW's FFT power spectrum and PSDVI, the autopower spectrum of temperature data is obtained; Smoothing of autopower spectrum data; The first-order derivative of the smoothed data is calculated to obtain the rate of change of the autopower spectrum with frequency; The minimum frequency value corresponding to the change rate tending to 0 at two consecutive points is the lowest noise frequency f N ; Decomposition level N: 2 N <fs / (2f N ), N takes the maximum value, fs is the sampling frequency; The wavelet transform is performed based on the WA 1D Discrete Wavelet Transform VI in LabVIEW. The number of decomposition levels is set to the desired value N, and the wavelet basis is selected as sym8 to obtain the wavelet coefficients. Keep the approximate coefficients and set all the detail coefficients to 0 to obtain a new set of wavelet coefficients; The inverse wavelet transform is performed with the new wavelet coefficients to obtain the filtered temperature signal.

4. The method according to claim 1, wherein Methods for obtaining the thermophysical parameters of the sample based on the preprocessed data include: Determine and calculate the actual pulse width τ; Calculate the reference temperature, i.e., the background temperature, and the temperature change ΔT; The values ​​of fitting parameters A and B; Calculate pulse power; Solve for thermal conductivity, thermal diffusivity, and specific heat capacity.

5. The method according to claim 4, characterized in that The method for determining and calculating the actual pulse width τ, calculating the reference temperature (ie, background temperature), and the temperature change ΔT includes: Through the waveform data of pulse voltage and loop current, the amplitude of each sampling point is directly read, that is, the pulse voltage U and loop current I at each sampling moment. According to the measurement principle, the actual pulse width τ and temperature change ΔT are calculated; The measurement principle is: Under a certain stable temperature and pressure environment, a thermal disturbance is given to the sample, and the temperature change ΔT is expressed as: Where n is the summation range. The larger the value of n, the higher the calculation accuracy. The value of n is determined according to the actual accuracy requirements. x represents the distance between the plane heat source and the thermocouple, and d represents the thickness of the sample in the heat conduction direction. Parameters A and B are defined as: Where P represents the pulse heating power, S represents the area of ​​the planar heat source, D represents the thermal diffusivity, and κ represents the thermal conductivity; Methods for fitting the values ​​of parameters A and B include: According to formula (1), the actual measured heating pulse width value τ is substituted. Based on the nonlinear fitting VI of LabVIEW, the formula after the substitution value is used as the formula model, the weight is set to 1, the initial values ​​of A and B are set to 1, the relative time t relative to the pulse trigger moment is used as the independent variable, and the ΔT value corresponding to time t is used as the dependent variable. Fitting is performed, and the best fitting parameters output by the VI are the values ​​of A and B.

6. The method according to claim 5, characterized in that The method for calculating pulse power includes: constructing a method for calculating pulse power by measuring pulse voltage U and loop current I in real time; When calculating the effective pulse power, the planar heat source is assumed to be a purely resistive load, then: P 总 Calculated by solving the integral method, the calculation formula is: Combining equations (3) and (4), the calculation formula for P heat source is as follows: Among them, R 线缆 In the pulse heating circuit, except for the plane heat source resistance R 热源 The sum of all resistances other than 总 is the total resistance of the pulse heating circuit.

7. The method according to claim 6, characterized in that R 总 The measurement methods include: The pulse voltage waveform curve is used to measure the end time of the pulse rising edge and the start time of the pulse falling edge. The pulse voltage U waveform and the loop current I waveform between the two moments are intercepted. According to R=U / I, the R of the intermediate period is obtained. 总 curve; Extract R 总 The 20 sampling points before and after the curve are used as the reference points for the resistance change trend; Based on the LabVIEW Generalized Polynomial VI, polynomial fitting is performed on each of the 20 sampling points. The polynomial order is set to 3, the fitting method is set to least squares, and the tolerance is set to 1E-6. This results in the third-order polynomial formulas for the front and back resistance curves, i.e., the curve formulas for the resistance change over time in the rising and falling segments. Substitute the sampling time value of the rising edge segment into the front-segment formula to calculate the resistance value of the front segment, and substitute the sampling time value of the falling edge segment into the back-segment formula to calculate the resistance value of the back segment; Connect the front, middle and rear resistance curves to get the complete R 总 Change curve.

8. The method according to claim 6, characterized in that Methods for solving for thermal conductivity, thermal diffusivity, and specific heat capacity include: According to the calculation formula of thermal conductivity κ and thermal diffusion coefficient D given in the measurement principle, thermal conductivity κ and thermal diffusion coefficient D are obtained and substituted into formula (6) to solve the specific heat capacity; Where ρ is the sample density.

9. A fully automatic measurement system for thermophysical parameters, the system being used to implement the method according to any one of claims 1 to 8, characterized in that: The system includes: an acquisition module, a pre-processing module, and a calculation module; The acquisition module is used to acquire the pulse voltage signal waveform data U applied to the planar heat source, the loop current signal waveform data I, and the thermocouple voltage waveform data representing the sample temperature; The preprocessing module is used to preprocess the collected data; The calculation module is used to obtain the thermophysical parameters of the sample based on the preprocessed data.

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