A method and device for detecting thermal physical parameters of a platinum film heat flux sensor substrate

Abstract

The present invention discloses a method and device for detecting the thermal property parameters of a platinum film heat flux sensor substrate. Step S1: Using a glycerin bath and an air bath for dual calibration comparison, a calibration expression for the thermal property parameters of the platinum film heat flux sensor substrate is derived to eliminate the influence of non-uniform thin films and the influence of inaccurate measurement of the film surface area, thereby improving the calibration accuracy. Step S2: Using a Wheatstone bridge in a glycerin bath and an air bath, the platinum film heat flux sensor to be tested is electrically measured to obtain calculated parameters of the thermal property parameters of the substrate, and the calculated parameters are substituted into the calibration expression to obtain the thermal property parameters of the platinum film heat flux sensor substrate to be tested. The present invention eliminates the influence of non-uniform thin films and the influence of inaccurate measurement of the film surface area, thereby improving the calibration accuracy, ensuring the accuracy of the calculated parameter measurement, and further improving the calibration accuracy of the thermal property parameters of the platinum film heat flux sensor substrate.

Description

Technical Field

[0001] The present invention relates to the technical field of thermophysical property measurement, and in particular to a method and device for detecting thermophysical property parameters of a platinum film heat flux sensor substrate. Background Art

[0002] In thermal environment tests of pulsed wind tunnel equipment such as shock wave wind tunnels, thin film heat flux sensors are usually used for heat flow measurement. This type of sensor has the advantages of high sensitivity, fast response, and small size. The heat flow measurement results are consistent with the thermophysical parameters of the sensor. Proportional to the material density, c is the material heat capacity, and k is the material thermal conductivity. The accuracy of the thermal flow sensor directly affects the accuracy of the heat flow measurement. Therefore, it is necessary to measure the thermal properties of the substrate material of the thin film heat flow sensor. Calibration can be performed by calibrating the material density, heat capacity and thermal conductivity respectively. For single materials whose composition can be precisely controlled (such as pure copper, stainless steel, etc.), high accuracy can be achieved through calibration. However, the base materials of thin-film thermal flow sensors are usually high borosilicate glass and ceramics. Even if the same raw materials and production processes are used, the material properties will vary. Therefore, it is necessary to obtain the material's thermal physical parameters through a comprehensive calibration method.

[0003] Currently, commonly used comprehensive calibration methods include heat flux calibration, transient heating, and immersion. Heat flux calibration involves calibrating the sensor surface heat flux and surface temperature response, and calculating the thermophysical properties. This calibration method is ultimately traceable to temperature measurement standards or current and voltage measurement standards. Developed countries have developed heat flux calibration devices, such as those at the National Institute of Standards and Technology (NIST) in the United States, the Swedish National Testing Institute, the Norwegian Fire Research Laboratory (SINTEF), the Italian National Metrology Institute (IMGC), and the National Laboratory of Metrology and Testing (LNE) in France. Domestic heat flux calibration devices include the blackbody furnace heat flux calibration device at the Institute of Ultra-High-Speed ​​Aerodynamics, China Aerodynamics Research and Development Center. Dai Jingmin et al. from Harbin Institute of Technology proposed using pulse heating technology to measure the thermophysical properties of materials. However, this method is not suitable for measuring the thermophysical properties of the substrate material of thin-film heat flux sensors. These aforementioned heat flux calibration devices are difficult to develop, costly, and complex to operate, making them unsuitable for pulsed aerodynamic heating measurements in shock tunnels. The transient heating and immersion calibration devices developed by the Institute of Ultra-High-Speed ​​Aerodynamics at the China Aerodynamics Research and Development Center have been well-received due to their simple experimental conditions and minimal equipment requirements. However, their drawbacks include an unstable heating source, demanding operating procedures, and significant repeatability errors caused by human factors. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for detecting the thermal physical parameters of a platinum film heat flow sensor substrate, so as to solve the technical problems of complex operation and low precision in the prior art.

[0005] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0006] A method for detecting thermal physical parameters of a platinum film heat flow sensor substrate comprises the following steps:

[0007] Step S1: deriving a calibration expression for the substrate thermophysical parameters of the platinum film heat flux sensor by performing a double calibration comparison using a glycerin bath and an air bath, thereby eliminating the influence of a non-uniform film and the influence of inaccurate measurement of the film surface area to improve the calibration accuracy. The substrate thermophysical parameters are composed of a combination of the density ρ, specific heat capacity c, and thermal conductivity k of the substrate material;

[0008] Step S2: Electrically measure the platinum film heat flux sensor to be tested using a Wheatstone bridge in a glycerin bath and an air bath to obtain calculation parameters of the substrate thermophysical properties, and substitute the calculation parameters into the calibration representation to obtain the thermophysical properties of the substrate of the platinum film heat flux sensor to be tested.

[0009] As a preferred embodiment of the present invention, the process of deriving the calibration representation formula of the substrate thermophysical property parameters includes:

[0010] The platinum film heat flow sensor is placed in an air bath for gas calibration. The surface heat flow rate formula of the platinum film heat flow sensor in the air medium is derived as follows:

[0011]

[0012] Where q0 is the surface heat flow rate of the platinum film heat flow sensor in the air medium, F is the constant parameter caused by the non-uniformity of the film in the platinum film heat flow sensor, (kρc) 1 / 2 is the thermal physical parameter of the substrate, k is the thermal conductivity coefficient of the substrate material, ρ is the density of the substrate material, c is the specific heat capacity of the substrate material, α is the resistance temperature coefficient of the platinum film heat flow sensor, I0 is the input current of the platinum film heat flow sensor, R0 is the initial film resistance of the platinum film heat flow sensor, E(t) is the potential of the platinum film heat flow sensor, and t is time;

[0013] The platinum film heat flow sensor that has completed air bath calibration is placed in a glycerin bath for liquid calibration, so that the surface heat flow rate of the platinum film heat flow sensor in the air medium is distributed as the surface heat flow rate of the platinum film heat flow sensor in the air medium and the liquid medium. The derived surface heat flow rate formula of the platinum film heat flow sensor in the air medium and the liquid medium is:

[0014]

[0015] Where mq0 is the surface heat flow rate of the platinum film heat flow sensor in the liquid medium, (1-m)q0 is the surface heat flow rate of the platinum film heat flow sensor in the air medium, m is the distribution ratio, E * (t) is the potential of the platinum film heat flow sensor in the liquid medium, [(kρc) 12 ] * is the thermal physical property parameter of liquid medium;

[0016] The surface heat flow rate of the platinum film heat flow sensor in the air medium and the liquid medium is summed to obtain:

[0017]

[0018] Will and Solving the equations together yields:

[0019]

[0020] E(t) and E * (t) is sent to the thermoelectric simulation network to obtain and Will Performing equivalent substitution to obtain the calibration expression is:

[0021]

[0022] Where V(t) is the potential E(t) of the platinum film heat flow sensor in the air medium, V * (t) is the potential E of the platinum film heat flow sensor in the liquid medium * (t).

[0023] As a preferred embodiment of the present invention, the liquid medium thermal property parameter [(kρc) 12 ] * The information can be obtained by querying the thermophysical property parameters according to the liquid medium type.

[0024] As a preferred solution of the present invention, the method of performing electrical measurement on the platinum film heat flow sensor to be detected using a Wheatstone bridge includes:

[0025] Place the platinum film heat flow sensor to be tested in the Wheatstone bridge as one arm of the Wheatstone bridge, and adjust the Wheatstone bridge to a balanced state;

[0026] Place the balanced Wheatstone bridge in an air bath for gas calibration to measure V(t) in the platinum film heat flow sensor to be tested. Then place the balanced Wheatstone bridge in a glycerin bath for liquid calibration to measure V in the platinum film heat flow sensor to be tested. *(t), will;

[0027] The V(t) in the platinum film heat flow sensor to be tested and the V * (t) Substitute the calibration formula into the above-mentioned equation to obtain the thermal physical property parameters of the platinum film heat flux sensor substrate to be tested.

[0028] As a preferred embodiment of the present invention, the V(t) in the platinum film heat flow sensor is * The measurement process of (t) includes:

[0029] The category features of the platinum film heat flow sensor, the air medium features of the air bath, and the liquid medium features of the glycerin bath are extracted, and the optimal measurement period of V(t) in the platinum film heat flow sensor is predicted using the pre-established air bath period prediction model, and the V(t) in the platinum film heat flow sensor is predicted using the pre-established glycerin bath period prediction model. * (t) the optimal measurement period;

[0030] At the optimal measurement period of V(t) and V * The optimal measurement period of (t) is used to measure V(t) in the platinum film heat flow sensor and V in the platinum film heat flow sensor. * (t) measurement to avoid measurement errors caused by the unstable state of the platinum film heat flux sensor in the air bath and glycerin bath;

[0031] The establishment of the air bath period prediction model includes:

[0032] A group of platinum film heat flux sensors of different categories are selected as sample sensors, and a group of air baths with different air medium characteristics are selected as sample air baths. Each Wheatstone bridge containing a sample sensor is placed in each sample air bath to perform real-time measurement of V(t) in the platinum film heat flux sensor. The measurement period when V(t) in the platinum film heat flux sensor is in a stable state is selected as the optimal measurement period.

[0033] The air bath period prediction model is obtained by using the CNN neural network to perform network training based on the CNN neural network input and output items, using the category features of the sample sensor and the air medium features of the sample air bath as CNN neural network input items, and using the optimal measurement period as CNN neural network output items;

[0034] The model expression of the air bath period prediction model is:

[0035] Time_gas=CNN(category,gas_feature);

[0036] Wherein, Time_gas represents the optimal measurement period of the air bath, category represents the category feature, gas_feature represents the air medium feature, and CNN represents the CNN neural network;

[0037] The establishment of the air bath period prediction model includes:

[0038] A group of glycerin baths with different liquid medium characteristics were selected as sample air baths. Each Wheatstone bridge containing a sample sensor was taken out of the sample air bath and then placed in each sample glycerin bath to measure the V in the platinum film heat flow sensor. * (t) real-time measurement and screening of V in platinum film heat flow sensor * (t) The measurement period in a stable state is taken as the optimal measurement period;

[0039] The CNN neural network is used to train the glycerin bath period prediction model by using the category features of the sample sensor and the liquid medium features of the sample glycerin bath as input items of the CNN neural network, and the optimal measurement period as output items of the CNN neural network.

[0040] The model expression of the glycerin bath period prediction model is:

[0041] Time_liquid=CNN(category,liquid_feature);

[0042] Wherein, Time_liquid represents the optimal measurement period of the glycerol bath, category represents the category feature, liquid_feature represents the liquid medium feature, and CNN represents the CNN neural network;

[0043] The stable state includes: the current of the sample sensor is maintained at a fixed value, and the sheet resistance of the sample sensor is maintained at a fixed value.

[0044] As a preferred embodiment of the present invention, the density, specific heat capacity and thermal conductivity of the liquid medium used in the glycerin bath are all known and are in a stable state.

[0045] As a preferred solution of the present invention, the Wheatstone bridge in equilibrium in the air bath and the glycerin bath applies a current of a fixed magnitude to the platinum film heat flow sensor to be detected.

[0046] As a preferred solution of the present invention, before network training, each feature component in the CNN neural network input item is normalized to eliminate dimensional errors.

[0047] As a preferred solution of the present invention, when liquid calibration is performed in a glycerin bath, the thin film of the platinum film heat flow sensor is completely immersed in the liquid medium.

[0048] As a preferred embodiment of the present invention, the present invention provides a detection device according to the method for detecting the thermal physical properties of the platinum film heat flux sensor substrate, comprising a Wheatstone bridge, an air bath device, a glycerin bath device and an auxiliary measurement tool. The Wheatstone bridge is used to provide a constant current source for the platinum film heat flux sensor to achieve constant heating of the platinum film heat flux sensor. The air bath device and the glycerin bath device are respectively used to provide a dual calibration environment for the platinum film heat flux sensor. The auxiliary measurement tool is used to perform auxiliary measurements in the Wheatstone bridge, the air bath device and the air bath device.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention utilizes a glycerin bath and an air bath for double calibration comparison to derive calibration expressions for the substrate thermophysical parameters of the platinum film heat flux sensor, thereby eliminating the influence of non-uniform thin films and the influence of inaccurate measurement of the film surface area to achieve improved calibration accuracy. In addition, a pre-established time period prediction model is utilized in the measurement of the calculated parameters of the platinum film heat flux sensor to predict the optimal measurement time period, thereby ensuring the accuracy of the calculated parameter measurement and further improving the calibration accuracy of the substrate thermophysical parameters of the platinum film heat flux sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0052] Figure 1 Flowchart of a method for detecting thermal physical property parameters of a platinum film heat flow sensor substrate provided by an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of a Wheatstone bridge circuit for calibrating a thin film resistance thermometer provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] 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.

[0055] like Figure 1 As shown, the present invention provides a method for detecting thermal physical parameters of a platinum film heat flow sensor substrate, comprising the following steps:

[0056] Step S1: Using a glycerin bath and an air bath for dual calibration comparison, a calibration expression for the substrate thermophysical parameters of the platinum film heat flux sensor is derived to eliminate the influence of non-uniform film and the influence of inaccurate measurement of film surface area, thereby improving calibration accuracy. The substrate thermophysical parameters are composed of the density ρ, specific heat capacity c, and thermal conductivity k of the substrate material;

[0057] The traditional calculation process is: under constant heating, the surface heat flow rate formula of the platinum film heat flow sensor is:

[0058]

[0059] If the heat flow rate It is known that the potential E(t) related to the surface temperature change is recorded experimentally, and the resistance temperature coefficient α, current I0, initial resistance R0 of the film, etc. are all accurately measured. Then the comprehensive thermal characteristics (kpc) 12 It can be done by Based on this consideration, the following calibration method is adopted.

[0060] The thin film resistance thermometer to be calibrated is used as one arm of the Wheatstone bridge, such as Figure 2 As shown, the bridge must be carefully and accurately balanced at the beginning, and then a constant current I0 is allowed to flow through the film. The electric power generated in the film is:

[0061]

[0062] The heating rate converted to thin film is:

[0063]

[0064] exist Figure 2 The relationship between voltage and resistance can be expressed as

[0065]

[0066] therefore

[0067]

[0068] There are

[0069] E0=I(t)[R0+R2+ΔR(t)] (8)

[0070] Substituting (8) into (7) we get

[0071]

[0072] Applying E(t) = I0·ΔR(t) to rewrite Equation (2) we can obtain

[0073]

[0074] Ignore the current change caused by the change of film resistance. Putting (4), (9) and (10) together we get

[0075]

[0076] During the calibration process, record E(t) and I(t). Under constant heating conditions, E(t) should be an ideal parabolic function. Therefore, the curve E(t) is fitted with a parabola using the least squares method to gradually improve the comprehensive thermal characteristics (kpc). 12 It can be obtained by equation (11). A more convenient way is to send E(t) into the thermoelectric simulation network, and the corresponding equation is

[0077]

[0078] Since V(t) is a constant under constant heat flow, equation (12) can be used to more easily calculate (kpc) 12 value.

[0079] However, using (11) or (12) to find (kpc 12 One of the biggest difficulties in measuring the value is that the surface area of ​​the film cannot be accurately measured, especially when the film surface is curved. In order to avoid measuring the surface area of ​​the film and other measurements that cause the combined thermal properties (kpc) 12 In order to avoid serious errors, this embodiment provides a double calibration method to eliminate the influence of non-uniform film and the influence of inaccurate measurement of film surface area and α, I0, and R0 values.

[0080] The process of deriving the calibration representation formula of the substrate thermophysical property parameters includes:

[0081] The platinum film heat flow sensor is placed in an air bath for gas calibration. The surface heat flow rate formula of the platinum film heat flow sensor in the air medium is derived as follows:

[0082]

[0083] Where q0 is the surface heat flow rate of the platinum film heat flow sensor in the air medium, F is the constant parameter caused by the non-uniformity of the film in the platinum film heat flow sensor, (kρc) 1 / 2is the thermal physical parameter of the substrate, k is the thermal conductivity coefficient of the substrate material, ρ is the density of the substrate material, c is the specific heat capacity of the substrate material, α is the resistance temperature coefficient of the platinum film heat flow sensor, I0 is the input current of the platinum film heat flow sensor, R0 is the initial film resistance of the platinum film heat flow sensor, E(t) is the potential of the platinum film heat flow sensor, and t is time;

[0084] The platinum film heat flow sensor that has completed air bath calibration is placed in a glycerin bath for liquid calibration, so that the surface heat flow rate of the platinum film heat flow sensor in the air medium is distributed as the surface heat flow rate of the platinum film heat flow sensor in the air medium and the liquid medium. The derived surface heat flow rate formula of the platinum film heat flow sensor in the air medium and the liquid medium is:

[0085]

[0086] Where mq0 is the surface heat flow rate of the platinum film heat flow sensor in the liquid medium, (1-m)q0 is the surface heat flow rate of the platinum film heat flow sensor in the air medium, m is the distribution ratio, E * (t) is the potential of the platinum film heat flow sensor in the liquid medium, [(kρc) 1 / 2 ] * is the thermal physical property parameter of liquid medium;

[0087] The surface heat flow rate of the platinum film heat flow sensor in the air medium and the liquid medium is summed to obtain:

[0088]

[0089] Will and Solving the equations together yields:

[0090]

[0091] E(t) and E * (t) is sent to the thermoelectric simulation network to obtain and Will Perform equivalent substitution to obtain the calibration expression:

[0092]

[0093] Where V(t) is the potential E(t) of the platinum film heat flow sensor in air, V * (t) is the potential E of the platinum film heat flow sensor in the liquid * (t).

[0094] Liquid medium thermal properties parameters [(kρc) 12 ] *The thermophysical parameters are obtained based on the type of liquid medium. Using the thermophysical parameters of the known uniform liquid, the thermophysical parameters of the platinum film heat flow sensor base can be obtained by comparing the potential difference between the platinum film heat flow sensor in air and in liquid under the same electric heating amount.

[0095] The calibration expression obtained in this embodiment no longer contains F, S, α, Ⅰ0, and R0, eliminating the influence of non-uniform films and the influence of inaccurate measurement of film surface area and α, I0, and R0 values, thereby improving the calibration accuracy of the substrate thermal physical properties parameters.

[0096] Step S2: Electrically measure the platinum film heat flux sensor to be tested using a Wheatstone bridge in a glycerin bath and an air bath to obtain calculation parameters of the substrate thermal physical properties, and substitute the calculation parameters into the calibration representation formula to obtain the thermal physical properties of the substrate of the platinum film heat flux sensor to be tested.

[0097] The Wheatstone bridge is used to perform electrical measurements on the platinum film heat flux sensor to be tested, including:

[0098] Place the platinum film heat flow sensor to be tested in the Wheatstone bridge as one arm of the Wheatstone bridge, and adjust the Wheatstone bridge to a balanced state;

[0099] Place the balanced Wheatstone bridge in an air bath for gas calibration to measure V(t) in the platinum film heat flow sensor to be tested. Then place the balanced Wheatstone bridge in a glycerin bath for liquid calibration to measure V in the platinum film heat flow sensor to be tested. * (t), will;

[0100] The V(t) in the platinum film heat flow sensor to be tested and the V * Substitute (t) into the calibration formula to obtain the thermal physical parameters of the platinum film heat flux sensor substrate to be tested.

[0101] V(t) in the platinum film heat flow sensor V(t) in the platinum film heat flow sensor * The measurement process of (t) includes:

[0102] The category features of the platinum film heat flow sensor, the air medium features of the air bath, and the liquid medium features of the glycerin bath are extracted, and the optimal measurement period of V(t) in the platinum film heat flow sensor is predicted using the pre-established air bath period prediction model, and the V(t) in the platinum film heat flow sensor is predicted using the pre-established glycerin bath period prediction model. * (t) the optimal measurement period;

[0103] At the optimal measurement period of V(t) and V * The optimal measurement period of (t) is used to measure V(t) in the platinum film heat flow sensor and V in the platinum film heat flow sensor.* (t) measurement to avoid measurement errors caused by the unstable state of the platinum film heat flux sensor in the air bath and glycerin bath;

[0104] The establishment of the air bath period prediction model includes:

[0105] A group of platinum film heat flux sensors of different categories are selected as sample sensors, and a group of air baths with different air medium characteristics are selected as sample air baths. Each Wheatstone bridge containing a sample sensor is placed in each sample air bath to perform real-time measurement of V(t) in the platinum film heat flux sensor. The measurement period when V(t) in the platinum film heat flux sensor is in a stable state is selected as the optimal measurement period.

[0106] The category features of the sample sensor and the air medium features of the sample air bath are used as CNN neural network input items, and the optimal measurement period is used as the CNN neural network output item. The CNN neural network is used to perform network training based on the CNN neural network input items and CNN neural network output items to obtain an air bath period prediction model.

[0107] The model expression of the air bath period prediction model is:

[0108] Time_gas=CNN(category,gas_feature);

[0109] Where Time_gas represents the optimal measurement period of the air bath, category represents the category feature, gas_feature represents the air medium feature, and CNN represents the CNN neural network;

[0110] The establishment of the air bath period prediction model includes:

[0111] A group of glycerin baths with different liquid medium characteristics were selected as sample air baths. Each Wheatstone bridge containing a sample sensor was taken out of the sample air bath and then placed in each sample glycerin bath to measure the V in the platinum film heat flow sensor. * (t) real-time measurement and screening of V in platinum film heat flow sensor * (t) The measurement period in a stable state is taken as the optimal measurement period;

[0112] The category features of the sample sensor and the liquid medium features of the sample glycerin bath are used as CNN neural network input items, and the optimal measurement period is used as the CNN neural network output item. The CNN neural network is used to train the network based on the CNN neural network input items and CNN neural network output items to obtain a glycerin bath period prediction model.

[0113] The model expression of the glycerol bath period prediction model is:

[0114] Time_liquid=CNN(category,liquid_feature);

[0115] Where Time_liquid represents the optimal measurement period of the glycerol bath, category represents the category feature, liquid_feature represents the liquid medium feature, and CNN represents the CNN neural network.

[0116] The stable state includes: the current of the sample sensor is maintained at a fixed value, and the sheet resistance of the sample sensor is maintained at a fixed value.

[0117] Predict an optimal measurement time to avoid the platinum film heat flux sensor not being stable during the air bath and glycerin bath measurements, resulting in the measured V(t) and V * The (t) value is unreliable, which ultimately affects the calibration accuracy. The prediction function is automatically realized by using the model to avoid human subjectivity and reduce human workload. There is no need for data screening, which also reduces the invalid data generated by a large number of invalid measurements.

[0118] The density, specific heat capacity and thermal conductivity of the liquid medium used in the glycerin bath are all known and the state is stable.

[0119] A fixed current is applied to the platinum film heat flux sensor to be detected by the Wheatstone bridge in the equilibrium state in the air bath and the glycerin bath.

[0120] Before network training, each feature component in the CNN neural network input is normalized to eliminate dimensional errors.

[0121] When liquid calibration is performed in a glycerin bath, the thin film of the platinum film heat flow sensor is completely immersed in the liquid medium.

[0122] Based on the above-mentioned method for detecting the thermal physical properties of the platinum film heat flux sensor substrate, the present invention provides a detection device, including a Wheatstone bridge, an air bath device, a glycerin bath device and an auxiliary measurement tool. The Wheatstone bridge is used to provide a constant current source for the platinum film heat flux sensor to achieve constant heating of the platinum film heat flux sensor. The air bath device and the glycerin bath device are respectively used to provide a dual calibration environment for the platinum film heat flux sensor. The auxiliary measurement tool is used to perform auxiliary measurements in the Wheatstone bridge, the air bath device and the air bath device.

[0123] The present invention utilizes a glycerin bath and an air bath for double calibration comparison to derive calibration expressions for the substrate thermophysical parameters of the platinum film heat flux sensor, thereby eliminating the influence of non-uniform thin films and the influence of inaccurate measurement of the film surface area to achieve improved calibration accuracy. In addition, a pre-established time period prediction model is utilized in the measurement of the calculated parameters of the platinum film heat flux sensor to predict the optimal measurement time period, thereby ensuring the accuracy of the calculated parameter measurement and further improving the calibration accuracy of the substrate thermophysical parameters of the platinum film heat flux sensor.

[0124] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. A method for detecting thermal physical parameters of a platinum film heat flow sensor substrate, characterized in that: The following steps are involved: Step S1: deriving a calibration expression for the substrate thermophysical parameters of the platinum film heat flux sensor by performing a double calibration comparison using a glycerin bath and an air bath, thereby eliminating the influence of a non-uniform film and the influence of inaccurate measurement of the film surface area to improve the calibration accuracy. The substrate thermophysical parameters are composed of a combination of the density ρ, specific heat capacity c, and thermal conductivity k of the substrate material; Step S2: Electrically measure the platinum film heat flux sensor to be tested using a Wheatstone bridge in a glycerin bath and an air bath to obtain calculation parameters of the substrate thermophysical properties, and substitute the calculation parameters into the calibration representation to obtain the thermophysical properties of the substrate of the platinum film heat flux sensor to be tested.

2. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 1, characterized in that: The process of deriving the calibration representation formula of the substrate thermophysical property parameters includes: The platinum film heat flow sensor is placed in an air bath for gas calibration. The surface heat flow rate formula of the platinum film heat flow sensor in the air medium is derived as follows: Where q0 is the surface heat flow rate of the platinum film heat flow sensor in the air medium, F is the constant parameter caused by the non-uniformity of the film in the platinum film heat flow sensor, (kρc) 1 / 2 is the thermal physical parameter of the substrate, k is the thermal conductivity coefficient of the substrate material, ρ is the density of the substrate material, c is the specific heat capacity of the substrate material, α is the resistance temperature coefficient of the platinum film heat flow sensor, I0 is the input current of the platinum film heat flow sensor, R0 is the initial film resistance of the platinum film heat flow sensor, E(t) is the potential of the platinum film heat flow sensor, and t is time; The platinum film heat flow sensor that has completed air bath calibration is placed in a glycerin bath for liquid calibration, so that the surface heat flow rate of the platinum film heat flow sensor in the air medium is distributed as the surface heat flow rate of the platinum film heat flow sensor in the air medium and the liquid medium. The derived surface heat flow rate formula of the platinum film heat flow sensor in the air medium and the liquid medium is: Where mq0 is the surface heat flow rate of the platinum film heat flow sensor in the liquid medium, (1-m)q0 is the surface heat flow rate of the platinum film heat flow sensor in the air medium, m is the distribution ratio, E * (t) is the potential of the platinum film heat flow sensor in the liquid medium, [(kρc) 1 / 2 ] * is the thermal physical property parameter of liquid medium; The surface heat flow rate of the platinum film heat flow sensor in the air medium and the liquid medium is summed to obtain: Will and Solving the equations together yields: E(t) and E * (t) is sent to the thermoelectric simulation network to obtain and Will Performing equivalent substitution to obtain the calibration expression is: Where V(t) is the potential E(t) of the platinum film heat flow sensor in the air medium, V * (t) is the potential E of the platinum film heat flow sensor in the liquid medium * (t).

3. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 2, characterized in that: The liquid medium thermal physical parameter [(kρc) 1 / 2 ] * The information can be obtained by querying the thermophysical property parameters according to the liquid medium type.

4. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 3, characterized in that: The method of performing electrical measurement on the platinum film heat flow sensor to be detected by using a Wheatstone bridge includes: Place the platinum film heat flow sensor to be tested in the Wheatstone bridge as one arm of the Wheatstone bridge, and adjust the Wheatstone bridge to a balanced state; Place the balanced Wheatstone bridge in an air bath for gas calibration to measure V(t) in the platinum film heat flow sensor to be tested. Then place the balanced Wheatstone bridge in a glycerin bath for liquid calibration to measure V in the platinum film heat flow sensor to be tested. * (t), will; The V(t) in the platinum film heat flow sensor to be tested and the V * (t) Substitute the calibration formula into the above-mentioned equation to obtain the thermal physical property parameters of the platinum film heat flux sensor substrate to be tested.

5. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 4, characterized in that: The V(t) in the platinum film heat flow sensor is the V in the platinum film heat flow sensor. * The measurement process of (t) includes: The category features of the platinum film heat flow sensor, the air medium features of the air bath, and the liquid medium features of the glycerin bath are extracted, and the optimal measurement period of V(t) in the platinum film heat flow sensor is predicted using the pre-established air bath period prediction model, and the V(t) in the platinum film heat flow sensor is predicted using the pre-established glycerin bath period prediction model. * (t) the optimal measurement period; At the optimal measurement period of V(t) and V * The optimal measurement period of (t) is used to measure V(t) in the platinum film heat flow sensor and V in the platinum film heat flow sensor. * (t) measurement to avoid measurement errors caused by the unstable state of the platinum film heat flux sensor in the air bath and glycerin bath; The establishment of the air bath period prediction model includes: A group of platinum film heat flux sensors of different categories are selected as sample sensors, and a group of air baths with different air medium characteristics are selected as sample air baths. Each Wheatstone bridge containing a sample sensor is placed in each sample air bath to perform real-time measurement of V(t) in the platinum film heat flux sensor. The measurement period when V(t) in the platinum film heat flux sensor is in a stable state is selected as the optimal measurement period. The air bath period prediction model is obtained by using the CNN neural network to perform network training based on the CNN neural network input and output items, using the category features of the sample sensor and the air medium features of the sample air bath as CNN neural network input items, and using the optimal measurement period as CNN neural network output items; The model expression of the air bath period prediction model is: Time_gas=CNN(category, gas_feature); Wherein, Time_gas represents the optimal measurement period of the air bath, category represents the category feature, gas_feature represents the air medium feature, and CNN represents the CNN neural network; The establishment of the air bath period prediction model includes: A group of glycerin baths with different liquid medium characteristics were selected as sample air baths. Each Wheatstone bridge containing a sample sensor was taken out of the sample air bath and then placed in each sample glycerin bath to measure the V in the platinum film heat flow sensor. * (t) real-time measurement and screening of V in platinum film heat flow sensor * (t) The measurement period in a stable state is taken as the optimal measurement period; The CNN neural network is used to train the glycerin bath period prediction model by using the category features of the sample sensor and the liquid medium features of the sample glycerin bath as input items of the CNN neural network, and the optimal measurement period as output items of the CNN neural network. The model expression of the glycerin bath period prediction model is: Time_liquid=CNN(category, liquid_feature); Wherein, Time_liquid represents the optimal measurement period of the glycerol bath, category represents the category feature, liquid_feature represents the liquid medium feature, and CNN represents the CNN neural network; The stable state includes: the current of the sample sensor is maintained at a fixed value, and the sheet resistance of the sample sensor is maintained at a fixed value.

6. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 5, characterized in that: The density, specific heat capacity and thermal conductivity of the liquid medium used in the glycerin bath are all known and are in a stable state.

7. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 6, characterized in that: The Wheatstone bridge in the equilibrium state in the air bath and the glycerin bath applies a fixed current to the platinum film heat flow sensor to be detected.

8. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 7, characterized in that: Before network training, each feature component in the CNN neural network input is normalized to eliminate dimensional errors.

9. The method for detecting thermal physical parameters of a platinum film heat flow sensor substrate according to claim 8, characterized in that: When liquid calibration is performed in a glycerin bath, the thin film of the platinum film heat flow sensor is completely immersed in the liquid medium.

10. A detection device for detecting the thermal physical property parameters of a platinum film heat flow sensor substrate according to any one of claims 1 to 9, characterized in that: It includes a Wheatstone bridge, an air bath device, a glycerin bath device and an auxiliary measurement tool. The Wheatstone bridge is used to provide a constant current source for the platinum film heat flux sensor to achieve constant heating of the platinum film heat flux sensor. The air bath device and the glycerin bath device are respectively used to provide a dual calibration environment for the platinum film heat flux sensor. The auxiliary measurement tool is used to perform auxiliary measurements in the Wheatstone bridge, the air bath device and the air bath device.

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