CN115371941A - Method and device for detecting thermophysical parameters of platinum film heat flow sensor substrate - Google Patents
Method and device for detecting thermophysical parameters of platinum film heat flow sensor substrate Download PDFInfo
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Abstract
The invention discloses a method and a device for detecting a substrate thermophysical property parameter of a platinum film heat flow sensor, and the method comprises the following steps of S1, deriving a calibration characterization formula of the substrate thermophysical property parameter of the platinum film heat flow sensor by using a glycerin bath and an air bath for double calibration comparison, so as to eliminate the influence of a non-uniform film and the influence of inaccurate measurement of the surface area of the film, and realize the improvement of calibration precision; and S2, electrically measuring the platinum film heat flow sensor to be detected in the glycerin bath and the air bath by using a Wheatstone bridge to obtain a calculation parameter of the substrate thermophysical property parameter, and substituting the calculation parameter into the calibration characterization formula to obtain the substrate thermophysical property parameter of the platinum film heat flow sensor to be detected. The invention eliminates the influence of non-uniform films and the influence of inaccurate measurement of the surface area of the film, realizes the improvement of the calibration precision, can ensure the accuracy of the measurement of the calculation parameters, and further improves the calibration precision of the thermophysical parameters of the substrate of the platinum film heat flow sensor.
Description
Technical Field
The invention relates to the technical field of thermophysical property measurement, in particular to a method and a device for detecting thermophysical property parameters of a platinum film heat flow sensor substrate.
Background
In the thermal environment test of impulse wind tunnel equipment such as shock tunnel, a thin film heat flow sensor is usually used for heat flow measurement, the sensor has the advantages of high sensitivity, quick response, small size and the like, and the heat flow measurement result and the thermophysical parameter of the sensor
Proportional ratio, where ρ is the material density, c is the material heat capacity, and k is the material thermal conductivity.
The accuracy of the heat flow measurement is directly affected. Thus, there is a need for thermophysical parameters of thin film heat flow sensor substrate materials
And (5) calibrating. Can be obtained by respectively calibrating the density, heat capacity and heat conductivity coefficient of the material
For a single material (such as pure copper, stainless steel and the like) with accurately controllable material components, high precision [1-2 ] can be obtained through calibration]. However, thin film heat flow sensor substrate materials typically employ borosilicate glass and ceramic, and even with the same raw materials and manufacturing processes, there are differences in material properties. Therefore, it is necessary to obtain the thermophysical property parameters of the material by a comprehensive calibration method
Currently, common comprehensive calibration methods include a heat flux calibration method, a transient heating method, and an immersion method [3]. The heat flow calibration method is to obtain the surface heat flow and the surface temperature response of the sensor through calibration, obtain thermophysical parameters through calculation, and finally trace the temperature measurement standard or the current and voltage measurement standard through the calibration method. Heat flux calibration devices were developed in time in developed countries, such as the National Institute of Standards and Technology (NIST) calibration device, the swedish national institute of testing and research (sweden), the norwegian fire research laboratory (SINTEF), the italian national measurement Institute (IMGC) calibration device, and the french national measurement and testing Laboratory (LNE) [4-7]. The domestic heat flow calibration device is a black body furnace heat flow calibration device of the China aerodynamic research and development center ultrahigh speed aerodynamic research institute and the like [8-9]. The Dacisian of Harbin Industrial university [10-11] proposed the use of pulse heating techniques to measure the thermophysical parameters of materials, but this method is not suitable for measuring the thermophysical parameters of thin film heat flow sensor substrate materials. The heat flow calibration devices are difficult to develop, high in construction cost and complex to operate, and are not suitable for shock tunnel pulse pneumatic heating measurement. The transient heating method and immersion method calibration device established by the ultrahigh-speed aerodynamic research institute of the China aerodynamic research and development center is well applied due to the simple experimental conditions and low requirements on experimental equipment. But the defects are that the heating source is unstable, the requirements on the operation process are strict, and the repeatability error caused by human factors is larger.
Disclosure of Invention
The invention aims to provide a method and a device for detecting thermophysical parameters of a substrate of a platinum film heat flow sensor, which aim to solve the technical problems of complex operation and low precision in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a method for detecting thermophysical parameters of a substrate of a platinum film heat flow sensor comprises the following steps:
s1, deriving a calibration characterization formula of a substrate thermophysical property parameter of the platinum film heat flow sensor by using a glycerin bath and an air bath for double calibration contrast so as to eliminate the influence of a non-uniform film and the influence of inaccurate measurement of the surface area of the film and realize the improvement of calibration precision, wherein the substrate thermophysical property parameter is formed by combining the density rho, the specific heat capacity c and the heat conduction coefficient k of a substrate material;
and S2, electrically measuring the platinum film heat flow sensor to be detected in the glycerin bath and the air bath by using a Wheatstone bridge to obtain a calculation parameter of the substrate thermophysical property parameter, and substituting the calculation parameter into the calibration characterization formula to obtain the substrate thermophysical property parameter of the platinum film heat flow sensor to be detected.
As a preferable scheme of the invention, the derivation process of the calibration characterization formula of the substrate thermophysical property parameter comprises the following steps:
placing the platinum film heat flow sensor in an air bath for gas calibration derivation to obtain a surface heat flow rate formula of the platinum film heat flow sensor in an air medium, wherein the formula comprises the following steps:
in the formula, q 0 Is the surface heat flow rate of the platinum film heat flow sensor in an air medium, F is a constant parameter caused by the non-uniformity of a film in the platinum film heat flow sensor, (k rho c) 1/2 Is a substrate thermophysical parameter, k is a substrate material heat conduction coefficient, rho is the density of the substrate material, c is the specific heat capacity of the substrate material, alpha is the resistance temperature coefficient of the platinum film heat flow sensor, I 0 For passing current, R, to the platinum film heat flow sensor 0 The initial resistance of the film of the platinum film heat flow sensor, E (t) is the potential of the platinum film heat flow sensor, and t is time;
placing the platinum film heat flow sensor which finishes air bath calibration in a glycerin bath for liquid calibration, so that the surface heat flow rate of the platinum film heat flow sensor on an air medium is distributed as the surface heat flow rate of the platinum film heat flow sensor on the air medium and a liquid medium, and deducing the surface heat flow rate formula of the platinum film heat flow sensor on the air medium and the liquid medium as follows:
in the formula, mq 0 For the surface heat flow rate of the platinum film heat flow sensor in a liquid medium, (1-m) q 0 The surface heat flow rate of the platinum film heat flow sensor in an air medium is obtained, m is a distribution proportion, E * (t) potential of platinum film heat flow sensor in liquid medium, [ (k ρ c) 1/2 ] * Is a liquid medium thermophysical parameter;
summing the surface heat flow rates of the platinum film heat flow sensor on the air medium and the liquid medium to obtain:
will be provided with
And
and performing simultaneous solution to obtain:
mixing E (t) and E * (t) feeding into a thermoelectric simulation network to obtain
And
will be provided with
Carrying out equivalent replacement to obtain the calibration characterization formula as follows:
in the formula, V (t) is the potential E (t) of the platinum film heat flow sensor in an air medium, and V * (t) is the potential E of the platinum film heat flow sensor in the liquid medium * (t)。
In a preferred embodiment of the present invention, the liquid medium has a thermophysical parameter [ (k ρ c) 1/2 ] * According to the type of the liquid medium, the thermal physical property parameters are inquired.
As a preferable aspect of the present invention, the electrically measuring the platinum film heat flow sensor to be detected by using a wheatstone bridge includes:
placing a platinum film heat flow sensor to be detected in a Wheatstone bridge to serve as one arm of the Wheatstone bridge, and adjusting the Wheatstone bridge to a balance state;
placing the balance Wheatstone bridge in an air bath for gas calibration to measure V (t) in the platinum film heat flow sensor to be detected, and then placing the balance Wheatstone bridge in a glycerin bath for liquid calibration to measure V in the platinum film heat flow sensor to be detected * (t) subjecting;
v (t) in the platinum film heat flow sensor to be detected and V in the platinum film heat flow sensor to be detected * And (t) bringing the measured values into the calibration characterization formula to obtain the thermophysical property parameters of the substrate of the platinum film heat flow sensor to be detected.
As a preferable scheme of the invention, V (t) in the platinum film heat flow sensor is V (t) in the platinum film heat flow sensor * The measuring process of (t) includes:
extracting the class characteristics of the platinum film heat flow sensor, the air medium characteristics of the air bath and the liquid medium characteristics of the glycerin bath, predicting the optimal measurement period of V (t) in the platinum film heat flow sensor by using a pre-established air bath period prediction model, and predicting V in the platinum film heat flow sensor by using a pre-established glycerin bath period prediction model * (t) an optimal measurement period;
optimal measurement period at V (t) and V, respectively * (t) optimal measurement period V (t) measurement in platinum film heat flow sensor and V in platinum film heat flow sensor * (t) to avoid measurement errors caused by the platinum film heat flow sensor being in an unstable state in the air bath and the glycerin bath;
the establishment of the air bath time period prediction model comprises the following steps:
selecting a group of platinum film heat flow sensors of different types as sample sensors, selecting a group of air baths with different air medium characteristics as sample air baths, placing each Wheatstone bridge containing the sample sensors in each sample air bath to perform real-time measurement of V (t) in the platinum film heat flow sensors, and screening out a measurement time period when V (t) in the platinum film heat flow sensors is in a stable state as an optimal measurement time period;
taking the category characteristics of the sample sensor and the air medium characteristics of the sample air bath as CNN neural network input items, taking the optimal measurement time interval as CNN neural network output items, and performing network training by using the CNN neural network based on the CNN neural network input items and the CNN neural network output items to obtain an air bath time interval prediction model;
the model expression of the air bath time period prediction model is as follows:
Time_gas＝CNN(category，gas_feature)；
in the formula, time _ gas is characterized as the optimal measurement Time period of the air bath, category is characterized as the category characteristic, gas _ feature is characterized as the air medium characteristic, and CNN is characterized as the CNN neural network;
the establishment of the air bath time period prediction model comprises the following steps:
selecting a group of glycerol baths with different liquid medium characteristics as sample air baths, taking out each Wheatstone bridge containing a sample sensor from the sample air baths, and placing each Wheatstone bridge in each sample glycerol bath for performing V in a platinum film heat flow sensor * (t) real-time measurement, and screening out V in the platinum film heat flow sensor * (t) a measurement period in a steady state as an optimal measurement period;
the category characteristics of the sample sensor and the liquid medium characteristics of the sample glycerin bath are used as CNN neural network input items, the optimal measurement time interval is used as CNN neural network output items, and the CNN neural network is used for carrying out network training based on the CNN neural network input items and the CNN neural network output items to obtain the glycerin bath time interval prediction model;
the model expression of the glycerol bath time period prediction model is as follows:
Time_liquid＝CNN(category，liquid)feature)；
in the formula, time _ liquid is characterized as the optimal measurement Time period of the glycerol bath, category is characterized as the category characteristic, liquid _ feature is characterized as the liquid medium characteristic, and CNN is characterized as the CNN neural network;
the steady state includes: the current of the sample sensor is maintained at a constant value, and the sheet resistance of the sample sensor is maintained at a constant value.
In a preferred embodiment of the present invention, the liquid medium used in the glycerin bath has a known density, specific heat capacity, and heat transfer coefficient, and is stable.
In a preferred embodiment of the present invention, the wheatstone bridge in an equilibrium state 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.
As a preferable scheme of the invention, before network training, normalization processing is carried out on each characteristic component in the CNN neural network input item to eliminate dimension errors.
In a preferred embodiment of the invention, the membrane of the platinum-membrane heat flow sensor is completely immersed in the liquid medium during the liquid calibration in the glycerol bath.
The detection device comprises a wheatstone bridge, an air bath device, a glycerin bath device and an auxiliary measuring tool, wherein the wheatstone bridge is used for providing a constant current source for the platinum film heat flow sensor so as to realize constant heating of the platinum film heat flow sensor, the air bath device and the glycerin bath device are respectively used for providing a double calibration environment for the platinum film heat flow sensor, and the auxiliary measuring tool is used for carrying out auxiliary measurement in the wheatstone bridge, the air bath device and the air bath device.
Compared with the prior art, the invention has the following beneficial effects:
the method utilizes the glycerin bath and the air bath to carry out double calibration contrast to deduce a calibration characterization formula of the substrate thermophysical property parameter of the platinum film heat flow sensor, eliminates the influence of a non-uniform film and the influence of inaccurate measurement of the surface area of the film to realize the improvement of the calibration precision, and utilizes a pre-established time period prediction model to predict the optimal measurement time period in the calculation parameter measurement of the platinum film heat flow sensor, thereby ensuring the accuracy of the calculation parameter measurement and further improving the calibration precision of the substrate thermophysical property parameter of the platinum film heat flow sensor.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow chart of a method for detecting a thermophysical parameter of a substrate of a platinum film heat flow sensor according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a Wheatstone bridge circuit for calibrating a thin film resistance thermometer according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIG. 1, the invention provides a method for detecting a thermophysical parameter of a substrate of a platinum film heat flow sensor, which comprises the following steps:
s1, deriving a calibration characterization formula of a substrate thermophysical property parameter of the platinum film heat flow sensor by using a glycerin bath and an air bath for double calibration contrast so as to eliminate the influence of a non-uniform film and the influence of inaccurate measurement of the surface area of the film and realize the improvement of calibration precision, wherein the substrate thermophysical property parameter is formed by combining the density rho, the specific heat capacity c and the heat conduction coefficient k of a substrate material;
the traditional calculation process is: under constant heating, the surface heat flow rate of the platinum film heat flow sensor is expressed as:
if the heat flow rate
It is known that the potential E (t) associated with the surface temperature variation at the same time is recorded experimentally, the temperature coefficient of resistance α, the current I 0 Initial resistance R of the film 0 Etc. are accurately determined. Then the combined thermal properties (kpc) 1/2 Can be made of
And (4) calculating the formula. In this regard, the following calibration method is employed.
By using a thin film resistance thermometer to be calibrated as one arm of a Wheatstone bridge, as shown in FIG. 2, the bridge must be carefully and accurately balanced initially and then a constant current I is applied 0 Flows through the membrane. The electrical power generated on the film was:
the heating rate for conversion to film was:
in FIG. 2, the relationship between voltage and resistance can be expressed as
Thus, it is possible to provide
And is also provided with
E 0 ＝I(t)[R 0 +R 2 +ΔR(t)] (8)
Substituting (8) into (7) to obtain
Application E (t) = I 0 Δ R (t) by rewriting the formula (2)
The current change due to the change in the sheet resistance is ignored. In parallel (4), (9) and (1) are
During calibration, E (t) and I (t) are noted. In the case of constant heating, E (t) should be an ideal parabolic function. Thus, the least squares fitted parabola is used to asymptotically fit curve E (t) to integrate the thermal properties (kpc) 1/2 It can be found from equation (11). It is more convenient to send E (t) into thermoelectric simulation network, and obtain the following formula
V (t) is a constant value due to constant heat flow. Equation (12) can be more easily solved for (kpc) 1/2 The value is obtained.
However, the formula (11) or (12) is used to obtain ((kpc) 1/2 One of the biggest difficulties with the value is that the surface area of the film cannot be accurately measured, especially when the surface of the film is curved. In order to avoid measuring the surface area of the film and the combined thermal properties (kpc) caused by other measurements 1/2 In order to eliminate the effects of non-uniform films and the determination of the surface area of the film, and alpha, I 0 、R 0 Influence of inaccurate measurement of values.
The derivation process of the calibration characterization formula of the substrate thermophysical property parameter comprises the following steps:
placing the platinum film heat flow sensor in an air bath for gas calibration and deducing to obtain a surface heat flow rate formula of the platinum film heat flow sensor in an air medium, wherein the surface heat flow rate formula is as follows:
in the formula, q 0 Is the surface heat flow rate of the platinum film heat flow sensor in an air medium, F is a constant parameter caused by the non-uniformity of a film in the platinum film heat flow sensor, (k rho c) 1/2 Is a substrate thermophysical parameter, k is a substrate material heat conduction coefficient, rho is the density of the substrate material, c is the specific heat capacity of the substrate material, alpha is the resistance temperature coefficient of the platinum film heat flow sensor, I 0 For passing current, R, to the platinum film heat flow sensor 0 The initial resistance of the film of the platinum film heat flow sensor, E (t) is the potential of the platinum film heat flow sensor, and t is time;
placing the platinum film heat flow sensor which is calibrated by the air bath into the glycerin bath for liquid calibration, so that the surface heat flow rate of the platinum film heat flow sensor on the air medium is distributed to be the surface heat flow rate of the platinum film heat flow sensor on the air medium and the liquid medium, and deducing the surface heat flow rate formula of the platinum film heat flow sensor on the air medium and the liquid medium as follows:
in the formula, mq 0 For the surface heat flow rate of the platinum film heat flow sensor in a liquid medium, (1-m) q 0 The surface heat flow rate of the platinum film heat flow sensor in the air medium, m is a distribution proportion, E * (t) potential of platinum film heat flow sensor in liquid medium, [ (k ρ c) 1/2 ] * Is a liquid medium thermophysical parameter;
summing the surface heat flow rates of the platinum film heat flow sensor on the air medium and the liquid medium to obtain:
will be provided with
And
and performing simultaneous solution to obtain:
mixing E (t) and E * (t) feeding into a thermoelectric simulation network to obtain
And
will be provided with
Equivalent replacement is carried out to obtain a calibration characterization formula as follows:
in the formula, V (t) is the potential E (t) of the platinum film heat flow sensor in air, and V * (t) is the potential E of the platinum film heat flow sensor in the liquid * (t)。
Liquid medium thermophysical property parameter [ (k rho c) 1/2 ] * According to the type of the liquid medium, the method is inquired by the thermophysical parameter, and the method utilizes the known methodAnd (3) homogenizing the thermophysical parameters of the liquid, and comparing the potential difference of the platinum film heat flow sensor in the air and the liquid under the same electric heating quantity to obtain the thermophysical parameters of the substrate of the platinum film heat flow sensor.
The calibration characterization formula obtained in this example does not contain F, S, alpha and I 0 、R 0 Eliminating the influence of non-uniform film and the determination of film surface area and alpha, I 0 、R 0 The influence of inaccurate measurement of the value improves the calibration precision of the thermal physical property parameter of the substrate.
And S2, electrically measuring the platinum film heat flow sensor to be detected in the glycerin bath and the air bath by using a Wheatstone bridge to obtain a calculation parameter of the substrate thermophysical property parameter, and substituting the calculation parameter into a calibration characterization formula to obtain the substrate thermophysical property parameter of the platinum film heat flow sensor to be detected.
The method for electrically measuring the platinum film heat flow sensor to be detected by utilizing the Wheatstone bridge comprises the following steps:
placing a platinum film heat flow sensor to be detected in a Wheatstone bridge to serve as one arm of the Wheatstone bridge, and adjusting the Wheatstone bridge to a balance state;
placing the balance Wheatstone bridge in an air bath for gas calibration to measure V (t) in the platinum film heat flow sensor to be detected, and then placing the balance Wheatstone bridge in a glycerin bath for liquid calibration to measure V in the platinum film heat flow sensor to be detected * (t) subjecting;
v (t) in the platinum film heat flow sensor to be detected and V in the platinum film heat flow sensor to be detected * And (t) bringing the measured thermal property parameters into a calibration characterization formula to obtain the thermal property parameters of the substrate of the platinum film heat flow sensor to be detected.
V (t) in platinum film heat flow sensor * The measuring process of (t) includes:
extracting the class characteristics of the platinum film heat flow sensor, the air medium characteristics of the air bath and the liquid medium characteristics of the glycerin bath, predicting the optimal measurement period of V (t) in the platinum film heat flow sensor by using a pre-established air bath period prediction model, and predicting the optimal measurement period of V (t) in the platinum film heat flow sensor by using a pre-established air bath period prediction modelV in platinum film heat flow sensor predicted by glycerol bath time period prediction model * (t) an optimal measurement period;
optimal measurement period at V (t) and V, respectively * (t) optimal measurement period V (t) measurement in platinum film heat flow sensor and V in platinum film heat flow sensor * (t) to avoid measurement errors caused by the platinum film heat flow sensor being in an unstable state in the air bath and the glycerin bath;
the establishment of the air bath time interval prediction model comprises the following steps:
selecting a group of platinum film heat flow sensors of different types as sample sensors, selecting a group of air baths with different air medium characteristics as sample air baths, placing each Wheatstone bridge containing the sample sensors in each sample air bath to perform real-time measurement of V (t) in the platinum film heat flow sensors, and screening out a measurement time period when V (t) in the platinum film heat flow sensors is in a stable state as an optimal measurement time period;
the category characteristics of the sample sensor and the air medium characteristics of the sample air bath are used as CNN neural network input items, the optimal measurement time interval is used as CNN neural network output items, and the CNN neural network is used for carrying out network training on the basis of the CNN neural network input items and the CNN neural network output items to obtain an air bath time interval prediction model;
the model expression of the air bath time period prediction model is as follows:
Time_gas＝CNN(category，gas_feature)；
in the formula, time _ gas is characterized as the optimal measurement Time interval of the air bath, category is characterized as the category characteristic, gas _ feature is characterized as the air medium characteristic, and CNN is characterized as the CNN neural network;
the establishment of the air bath time interval prediction model comprises the following steps:
selecting a group of glycerol baths with different liquid medium characteristics as sample air baths, taking out each Wheatstone bridge containing a sample sensor from the sample air baths, and placing each Wheatstone bridge in each sample glycerol bath for performing V in a platinum film heat flow sensor * (t) real-time measurement, and screening out V in the platinum film heat flow sensor * (t) in a steady stateThe measurement period is used as an optimal measurement period;
the method comprises the following steps of taking the class characteristics of a sample sensor and the liquid medium characteristics of a sample glycerol bath as CNN neural network input items, taking the optimal measurement time interval as CNN neural network output items, and carrying out network training by using the CNN neural network based on the CNN neural network input items and the CNN neural network output items to obtain a glycerol bath time interval prediction model;
the model expression of the glycerol bath time period prediction model is as follows:
Time_liquid＝CNN(category，liquid_feature)；
in the formula, time _ liquid is characterized as the optimal measurement Time period of the glycerol bath, category is characterized as the category characteristic, liquid _ feature is characterized as the liquid medium characteristic, and CNN is characterized as the CNN neural network;
the steady state includes: the current of the sample sensor is maintained at a constant value, and the sheet resistance of the sample sensor is maintained at a constant value.
An optimal measurement time is predicted to avoid instability of the platinum film heat flow sensor in the air bath and glycerin bath measurement process, which results in measured V (t) and V * The value (t) is unreliable, the calibration precision is influenced finally, the prediction function is automatically realized by using the model, the artificial subjectivity is avoided, the artificial workload is reduced, data screening is not needed, and a large amount of invalid data generated by invalid measurement is reduced.
The liquid medium used in the glycerin bath has a known density, specific heat capacity, and heat transfer coefficient, and is stable.
The Wheatstone bridges in an equilibrium state in the air bath and the glycerin bath apply current with fixed magnitude to the platinum film heat flow sensor to be detected.
Before network training, normalization processing is carried out on each characteristic component in the CNN neural network input item so as to eliminate dimension errors.
The thin film of the platinum film heat flow sensor was completely immersed in the liquid medium during liquid calibration in the glycerol bath.
Based on the method for detecting the substrate thermophysical property parameters of the platinum film heat flow sensor, the invention provides a detection device which comprises a Wheatstone bridge, an air bath device, a glycerin bath device and an auxiliary measuring tool, wherein the Wheatstone bridge is used for providing a constant current source for the platinum film heat flow sensor so as to realize the constant heating of the platinum film heat flow sensor, the air bath device and the glycerin bath device are respectively used for providing a double calibration environment for the platinum film heat flow sensor, and the auxiliary measuring tool is used for carrying out auxiliary measurement in the Wheatstone bridge, the air bath device and the air bath device.
The method utilizes the glycerin bath and the air bath to carry out double calibration contrast to deduce a calibration characterization formula of the substrate thermophysical property parameter of the platinum film heat flow sensor, eliminates the influence of a non-uniform film and the influence of inaccurate measurement of the surface area of the film to realize the improvement of the calibration precision, and utilizes a pre-established time period prediction model to predict the optimal measurement time period in the calculation parameter measurement of the platinum film heat flow sensor, thereby ensuring the accuracy of the calculation parameter measurement and further improving the calibration precision of the substrate thermophysical property parameter of the platinum film heat flow sensor.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made to the disclosure by those skilled in the art within the spirit and scope of the disclosure, and such modifications and equivalents should also be considered as falling within the scope of the disclosure.
Claims (10)
1. A method for detecting thermophysical parameters of a substrate of a platinum film heat flow sensor is characterized by comprising the following steps:
s1, deriving a calibration characterization formula of a substrate thermophysical parameter of the platinum film heat flow sensor by using a glycerin bath and an air bath for double calibration comparison so as to eliminate the influence of a non-uniform film and the influence of inaccurate measurement of the surface area of the film and realize the improvement of calibration precision, wherein the substrate thermophysical parameter is formed by combining the density rho, the specific heat capacity c and the heat conduction coefficient k of a substrate material;
and S2, electrically measuring the platinum film heat flow sensor to be detected in the glycerin bath and the air bath by utilizing a Wheatstone bridge to obtain a calculation parameter of the substrate thermophysical property parameter, and substituting the calculation parameter into the calibration characterization formula to obtain the substrate thermophysical property parameter of the platinum film heat flow sensor to be detected.
2. The method for detecting the thermophysical property parameter of the substrate of the platinum film heat flow sensor as claimed in claim 1, wherein the method comprises the following steps: the derivation process of the calibration characterization formula of the substrate thermophysical property parameter comprises the following steps:
placing the platinum film heat flow sensor in an air bath for gas calibration derivation to obtain a surface heat flow rate formula of the platinum film heat flow sensor in an air medium, wherein the formula comprises the following steps:
in the formula, q 0 Is the surface heat flow rate of the platinum film heat flow sensor in an air medium, F is a constant parameter caused by the non-uniformity of a film in the platinum film heat flow sensor, (k rho c) 1/2 Is a substrate thermophysical parameter, k is a substrate material heat conduction coefficient, rho is the density of the substrate material, c is the specific heat capacity of the substrate material, alpha is the resistance temperature coefficient of the platinum film heat flow sensor, I 0 For the platinum film heat flow sensor, R 0 The initial resistance of the film of the platinum film heat flow sensor, E (t) is the potential of the platinum film heat flow sensor, and t is time;
placing the platinum film heat flow sensor which is calibrated by the air bath into the glycerin bath for liquid calibration, so that the surface heat flow rate of the platinum film heat flow sensor on the air medium is distributed to be the surface heat flow rate of the platinum film heat flow sensor on the air medium and the liquid medium, and deducing the surface heat flow rate formula of the platinum film heat flow sensor on the air medium and the liquid medium as follows:
in the formula, mq 0 For the surface heat flow rate of the platinum film heat flow sensor in a liquid medium, (1-m) q 0 The surface heat flow rate of the platinum film heat flow sensor in an air medium is obtained, m is a distribution proportion, E * (t) potential of platinum film heat flow sensor in liquid medium, [ (k ρ c) 1/2 ] * Is a liquid medium thermophysical parameter;
summing the surface heat flow rates of the platinum film heat flow sensor on the air medium and the liquid medium to obtain:
will be provided with
And
and performing simultaneous solution to obtain:
mixing E (t) and E * (t) feeding into a thermoelectric simulation network to obtain
And
will be provided with
Performing equivalent replacement to obtain the calibration characterization formula as follows:
in the formula, V (t) is the potential E (t) of the platinum film heat flow sensor in an air medium, and V * (t) is the potential E of the platinum film heat flow sensor in the liquid medium * (t)。
3. The method for detecting the thermophysical parameters of the substrate of the platinum film heat flow sensor as claimed in claim 2, wherein the method comprises the following steps: the liquid medium thermophysical property parameter [ (k rho c) 1/2 ] * According to the type of the liquid medium, the thermal physical property parameters are inquired.
4. The method for detecting the thermophysical property parameter of the substrate of the platinum film heat flow sensor as claimed in claim 3, wherein the method comprises the following steps: the method for electrically measuring the platinum film heat flow sensor to be detected by utilizing the Wheatstone bridge comprises the following steps:
placing a platinum film heat flow sensor to be detected in a Wheatstone bridge to serve as one arm of the Wheatstone bridge, and adjusting the Wheatstone bridge to a balance state;
placing the balance Wheatstone bridge in an air bath for gas calibration to measure V (t) in the platinum film heat flow sensor to be detected, and then placing the balance Wheatstone bridge in a glycerin bath for liquid calibration to measure V in the platinum film heat flow sensor to be detected * (t) subjecting;
v (t) in the platinum film heat flow sensor to be detected and V in the platinum film heat flow sensor to be detected * And (t) bringing the measured temperature into the calibration characterization formula to obtain the thermophysical property parameters of the substrate of the platinum film heat flow sensor to be detected.
5. The method for detecting the thermophysical property parameter of the substrate of the platinum film heat flow sensor as claimed in claim 4, wherein the method comprises the following steps: v (t) in the platinum film heat flow sensor * The measuring process of (t) includes:
extracting class characteristics of platinum film heat flow sensor and air medium characteristics of air bath and liquid medium characteristics of glycerin bath, utilizing pre-established air bath time periodPredicting an optimal measurement time period of V (t) in the platinum film heat flow sensor by using a prediction model, and predicting V (t) in the platinum film heat flow sensor by using a pre-established glycerol bath time period prediction model * (t) an optimal measurement period;
optimal measurement periods at V (t) and V, respectively * (t) optimal measurement period V (t) measurement in platinum film heat flow sensor and V in platinum film heat flow sensor * (t) measuring to avoid measurement errors caused by the unstable state of the platinum film heat flow sensor in the air bath and the glycerin bath;
the establishment of the air bath time period prediction model comprises the following steps:
selecting a group of platinum film heat flow sensors of different types as sample sensors, selecting a group of air baths with different air medium characteristics as sample air baths, placing each Wheatstone bridge containing the sample sensors in each sample air bath to perform real-time measurement of V (t) in the platinum film heat flow sensors, and screening out a measurement time period when V (t) in the platinum film heat flow sensors is in a stable state as an optimal measurement time period;
taking the category characteristics of the sample sensor and the air medium characteristics of the sample air bath as CNN neural network input items, taking the optimal measurement time interval as CNN neural network output items, and performing network training by using the CNN neural network based on the CNN neural network input items and the CNN neural network output items to obtain an air bath time interval prediction model;
the model expression of the air bath time period prediction model is as follows:
Time_gas＝CNN(category，gas_feature)；
in the formula, time _ gas is characterized as the optimal measurement Time interval of the air bath, category is characterized as the category characteristic, gas _ feature is characterized as the air medium characteristic, and CNN is characterized as the CNN neural network;
the establishment of the air bath time period prediction model comprises the following steps:
selecting a group of glycerol baths with different liquid medium characteristics as sample air baths, taking out each Wheatstone bridge containing a sample sensor from the sample air baths, and placing each Wheatstone bridge in each sample glycerol bath for platinum film heat flow sensorV of * (t) real-time measurement, and screening out V in the platinum film heat flow sensor * (t) a measurement period in a steady state as an optimal measurement period;
the category characteristics of the sample sensor and the liquid medium characteristics of the sample glycerin bath are used as CNN neural network input items, the optimal measurement time interval is used as CNN neural network output items, and the CNN neural network is used for carrying out network training based on the CNN neural network input items and the CNN neural network output items to obtain the glycerin bath time interval prediction model;
the model expression of the glycerol bath time period prediction model is as follows:
Time_liquid＝CNN(category，liquid_feature)；
in the formula, time _ liquid is characterized as the optimal measurement Time period of the glycerol bath, category is characterized as the category characteristic, liquid _ feature is characterized as the liquid medium characteristic, and CNN is characterized as the CNN neural network;
the steady state includes: the current of the sample sensor is maintained at a constant value, and the sheet resistance of the sample sensor is maintained at a constant value.
6. The method for detecting the thermophysical property parameter of the substrate of the platinum film heat flow sensor as claimed in claim 5, wherein the method comprises the following steps: the liquid medium used in the glycerol bath has a known density, specific heat capacity and heat transfer coefficient and is stable.
7. The method for detecting the thermophysical parameters of the substrate of the platinum film heat flow sensor as claimed in claim 6, wherein the method comprises the following steps: the Wheatstone bridges in an equilibrium state in the air bath and the glycerin bath apply current with fixed magnitude to the platinum film heat flow sensor to be detected.
8. The method for detecting the thermophysical property parameter of the substrate of the platinum film heat flow sensor as claimed in claim 7, wherein before network training, normalization processing is carried out on each characteristic component in CNN neural network input items so as to eliminate dimensional errors.
9. The method for detecting the thermophysical property parameter of the substrate of the platinum film heat flow sensor as claimed in claim 8, wherein the thin film of the platinum film heat flow sensor is completely immersed in a liquid medium when liquid calibration is carried out in a glycerol bath.
10. The device for detecting the substrate thermophysical property parameter of the platinum film heat flow sensor according to any one of claims 1 to 9, comprises a wheatstone bridge, an air bath device, a glycerin bath device and an auxiliary measuring tool, wherein the wheatstone bridge is used for providing a constant current source for the platinum film heat flow sensor so as to realize constant heating of the platinum film heat flow sensor, the air bath device and the glycerin bath device are respectively used for providing a dual calibration environment for the platinum film heat flow sensor, and the auxiliary measuring tool is used for carrying out auxiliary measurement in the wheatstone bridge, the air bath device and the air bath device.
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| CN118225375B (en) * | 2024-05-11 | 2024-07-16 | 中国航空工业集团公司沈阳空气动力研究所 | Intelligent calibration method for wide-frequency response heat flow sensor |
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