Small long-time measurement heat flow sensor suitable for two inverse problem algorithms and measurement method

Abstract

The invention provides a small-sized long-time heat flow measurement sensor and a measurement method suitable for two inverse problem algorithms. The small-sized long-time heat flow measurement sensor and the measurement method are used for measuring heat flow by using a calibration integral equation method and a space propulsion method. The sensor comprises a shell, a heat insulation material, a heat conduction material, a first thermocouple and a second thermocouple, the shell is of a cylindrical structure with an opening in one end, a cylindrical heat conduction material which is coaxially arranged is arranged in the shell, the first thermocouple and the second thermocouple are arranged between the heat conduction material and the shell, and the collecting ends of the first thermocouple and the second thermocouple are embedded into the heat conduction material; and other areas between the heat conduction material and the shell are filled with a heat insulation material. The sensor is compact in structure and small in size, and can be embedded in a material for measurement. And meanwhile, the sensor has relatively high precision and stability, can accurately output a prediction solution of a measuring point in short-time or long-time measurement, and has a relatively large application range and practicability.

Description

Technical Field

[0001] The present invention belongs to the field of thermal measurement, and relates to a heat flux sensor, in particular to a small-sized long-time measurement heat flux sensor and a measurement method applicable to two inverse problem algorithms. Background Art

[0002] With the development of society and the progress of technology, a large number of heat transfer problems to be solved have emerged in fields such as industrial and agricultural production, scientific research, aerospace, and energy power. As a core parameter describing the heat transfer process, accurately measuring and evaluating the heat flux distribution of the measured system can provide key thermal information for heat transfer problems, which is of great significance for optimizing thermal protection design and improving system performance. The increasing demand for heat flux data poses higher requirements for the functionality of heat flux sensors.

[0003] The inverse heat conduction problem is a type of problem that studies the inversion prediction of surface heat flux. It refers to the special situation where when the heated outer surface is in an extremely high-temperature environment, since the sensors arranged on the outer surface will not be able to work or be burned through, or it is difficult to install sensors in a narrow space, etc., the sensors can only be buried inside the material, and the heat flux and temperature conditions on the outer surface are inversely obtained through the internal temperature measurement data. Currently, there are many calibration methods and numerical methods for solving the inverse heat conduction problem, such as the conjugate gradient method, the Bayesian method, the reverse Monte Carlo method, the calibration integral equation method, the space marching method, etc. Gradient-based algorithms have numerous steps, a large amount of calculation, a long time required for solution, and a small deviation in the calculation process may lead to a large difference in the results; the Bayesian method also requires a large amount of calculation and is highly dependent on prior information. In the case of insufficient prior information, the error of the deduced results is relatively large.

[0004] The calibration integral equation method (CIEM) and the space marching method (SMM) are two commonly used solution methods for inverse heat conduction problems. Different from the above two methods with long iteration times and large limitations, both CIEM and SMM can quickly give accurate predicted solutions for the temperature and heat flux on the measurement surface based on the data of internal points without repeated iteration. CIEM does not depend on system parameters such as the thermal physical properties of the experimental body material, the internal embedding position of thermocouples, and the dynamic response time. It does not need to repeatedly iterate to obtain the optimal solution, but completes the prediction of the heat flux density or temperature on the reconstructed experimental surface through a pre-set calibration experiment, and quickly provides an accurate predicted solution. SMM, on the other hand, directly performs inversion through the heat flux and temperature of internal points without a calibration experiment when the thermal physical properties of the main body material and the internal embedding position of thermocouples are known. It can quickly give predicted solutions for the heat flux and temperature on the measurement surface without repeated iteration. At present, CIEM and SMM have been able to solve various types of inverse heat conduction problems and have achieved success. However, there is currently no heat flux sensor adapted to the CIEM and SMM algorithms, which limits the practical application scope of these two inverse problem algorithms.

[0005] Among various types of heat flux sensors that have been studied so far, coaxial thermocouples, circular foil heat flux sensors, Schmidt-Boelter heat flux sensors, and thermopile heat flux sensors are relatively common. The advantage of coaxial thermocouples is that they can perform point measurement and achieve internal measurement. The main disadvantage is that they cannot perform long-term continuous heat measurement. When the circular foil heat flux sensor works in a high-temperature environment, an external water cooling device needs to be connected. This sensor is large in size, has complex installation steps, and has a large interference on the thermal environment, so its application environment is limited. The Schmidt-Boelter heat flux sensor has high output, good linearity, and a wide dynamic range, but its response time to rapidly changing heat fluxes is long. The thermopile heat flux sensor has the advantages of a wide measurement range, good stability, and high accuracy, and is widely used in the measurement of heat flux density in high-temperature and harsh environments. However, its process is cumbersome and the cost is high. To sum up, the commonly used heat flux sensors currently generally have problems such as large size, long response time, inability to measure for a long time, and high cost.

[0006] To solve the deficiencies existing in the measurement of heat flux density by the above heat flux sensors, the present invention designs a heat flux sensor based on the CIEM and SMM algorithms, which can be embedded into the material to be measured and accurately measure the heat flux density at the measurement point for a long time. Summary of the Invention

[0007] To solve the problems in the prior art that the thermal flux sensor has a large size, cannot achieve internal point measurement, and most of them cannot meet the requirements of high precision, low cost, and long-term measurement, the present invention proposes a small long-term measurement thermal flux sensor and a measurement method applicable to two inverse problem algorithms. The present invention takes CIEM and SMM as the core and designs a small and built-in sensor that can measure the thermal flux density and temperature for a long time. The sensor has a compact structure and small size, and can be buried inside the material for measurement. At the same time, due to the superiority of CIEM and SMM, the sensor has high precision and stability, can quickly output accurate prediction solutions of the measurement points in both short-term and long-term measurements, and has a large application range and practicality.

[0008] The technical solution adopted by the present invention is as follows:

[0009] A small long-term measurement thermal flux sensor applicable to two inverse problem algorithms, the sensor includes a housing, heat insulation material, heat conduction material, a first thermocouple and a second thermocouple; the housing is a cylindrical structure with one end open, and a coaxial cylindrical heat conduction material is provided inside the housing, and the first thermocouple and the second thermocouple are provided between the heat conduction material and the housing, and the acquisition ends of the first thermocouple and the second thermocouple are embedded in the heat conduction material, and the other areas between the heat conduction material and the housing are filled with heat insulation material.

[0010] Further, the first thermocouple and the second thermocouple are exposed thermocouples.

[0011] Further, a first measurement point and a second measurement point are provided on the central axis inside the heat conduction material, and a first hole and a second hole are respectively provided in the radial direction of the heat conduction material at the first measurement point and the second measurement point, and the acquisition ends of the first thermocouple and the second thermocouple are respectively embedded in the first hole and the second hole.

[0012] Further, the acquisition ends of the first thermocouple and the second thermocouple are both wrapped with a silicone grease layer and are respectively fixed in the first hole and the second hole by an adhesive. The adhesive includes a high-temperature heat-conducting adhesive (Graphi-Bond669 glue) and a high-viscosity two-phase adhesive (CERAMABOND 571 adhesive). The high-temperature heat-conducting adhesive is poured into the first hole and the second hole, and the high-viscosity two-phase adhesive is coated on the surface of the high-temperature heat-conducting adhesive to reinforce the first thermocouple and the second thermocouple.

[0013] Further, the heat conduction material includes a measurement end and a tail end. The measurement end is aligned with the open end of the housing, and the tail end is aligned with the sealed end of the housing; the distance between the first measurement point and the measurement end is 10-20 mm; the distance between the second measurement point and the measurement end is 40-45 mm.

[0014] Furthermore, the diameter of the thermal conductive material is 2.5-4 mm, and the length is 45-55 mm; the thickness of the thermal insulating material is 1-1.25 mm; the outer diameter of the shell is 5-7 mm, the thickness is 0.5-0.7 mm, and the length is 45-55 mm.

[0015] Furthermore, two openings are provided on the bottom surface of the sealed end of the shell, and the leads of the first thermocouple and the second thermocouple are led along the axial direction between the heat-conducting material and the shell to the tail end of the heat-conducting material, and are led to the outside of the shell through the two openings respectively.

[0016] Furthermore, the heat insulating material is mullite to ensure that one-dimensional heat conduction occurs in the heat conducting material 3 during the measurement process. The heat conducting material 3 is stainless steel.

[0017] Furthermore, when the temperature of the measured point is below 600°C, the sensor is suitable for the calibration integral equation method; when the measured object point is at or above 600°C, the sensor is suitable for the space propulsion method.

[0018] A heat flow measurement method based on two inverse problem algorithms, implemented using the above sensor, includes the following steps:

[0019] The sensor is connected to a data acquisition board to obtain temperature data; the data acquisition board is connected to a computer to transmit the temperature data to the computer; the computer has a built-in calibration integral equation algorithm and a space propulsion algorithm;

[0020] The algorithm to be used is selected according to the temperature data of the measured point. When the temperature is below 600 ℃, the calibration integral equation algorithm is selected; when the temperature is above 600 ℃, the space advancement algorithm is selected;

[0021] After post-processing the temperature data using the selected algorithm, the predicted values ​​of the temperature and heat flux at the measuring point are output and an image is generated.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] (1) Compared with coaxial thermocouples, thanks to the accuracy and stability of CIEM and SMM algorithms, the present invention can realize heat flow measurement over a long period of time, making up for the defect that the former is only suitable for short-term measurement, and has a wider range of application scenarios.

[0024] (2) Compared with sensors with complex structures and larger sizes such as circular foil heat flux meters, the present invention has the structural advantages of a slender diameter and a small volume. It can be perforated to insert the probe into the interior of the object to be measured, achieving the measurement of heat flux data at internal points without affecting the measurement accuracy within the error range, which better meets the application characteristics and practical requirements in industrial production.

[0025] (3) Compared with methods for solving inverse heat conduction problems such as the conjugate gradient method and the Bayesian method, the CIEM and SMM algorithms do not require repeated iterations, have a smaller computational amount, and a faster solution speed, which is more than ten times that of other inverse problem algorithms. Moreover, unlike the Bayesian method that requires a large amount of prior information, the CIEM and SMM algorithms have fewer dependence conditions. The CIEM algorithm only requires the temperatures at two measurement points and calibration experiment data, and the SMM only requires the temperatures at two measurement points, the thermal physical property parameters of the main material, and the internal embedding position of the thermocouple, which is very flexible and lightweight. In addition, due to the high accuracy of the predicted solution of the CIEM under linear conditions and its fast solution speed, while the SMM can invert a more accurate predicted solution regardless of whether the thermal physical property parameters are linear or not, but with a slightly slower solution speed. The combination of the two can obtain accurate solutions more quickly in a wider temperature range.

[0026] (4) The present invention can not only measure the heat flux but also obtain the temperature data at the measured location simultaneously.

[0027] (5) The present invention can be conveniently transmitted and interacted with a computer. The temperature data of the thermocouple is directly connected to the computer through leads. After being processed by the algorithm, the specific data and variation curves of the heat flux and temperature of the measured point over time during the measurement period can be displayed on the computer, facilitating subsequent digital processing of experimental measurements.

[0028] (6) Benefiting from the superiority of the CIEM and SMM, the heat flux sensor of the present invention does not require a complex and precise structure, has a simple structure, a low cost, a wide application range, and is suitable for mass production. Description of the Drawings

[0029] Figure 1 It is a schematic cross-sectional structure diagram of the heat flux sensor in the embodiment of the present invention.

[0030] Figure 2 It is a three-dimensional schematic diagram of the end view of the heat flux sensor in the embodiment of the present invention.

[0031] Figure 3 It is a front view of the measurement end of the heat flux sensor in the embodiment of the present invention.

[0032] Figure 4 It is a partial enlarged view of the thermocouple temperature measurement point of the heat flux sensor in the embodiment of the present invention.

[0033] Figure 5This is the flowchart for the use of the heat flux sensor in the embodiments of the present invention.

[0034] Figure 6 This is the surface heat flux inverted by the CIEM algorithm in the embodiments of the present invention effect picture.

[0035] Figure 7 This is the surface heat flux inverted by the SMM algorithm in the embodiments of the present invention effect picture.

[0036] Figure 8 This is the surface temperature inverted by the SMM algorithm in the embodiments of the present invention effect picture.

[0037] Figure 9 These are the parameters used when the SMM algorithm is inverted in the embodiments of the present invention.

[0038] Among them, 1 is the outer shell, 2 is the heat insulation material, 3 is the heat conduction material, 4 is the first thermocouple, 5 is the second thermocouple, ① is the first measurement point, and ② is the second measurement point. Detailed implementation method

[0039] The technical solution of the present invention will be further clearly and detailedly described below in conjunction with the attached drawings and specific examples.

[0040] Embodiment

[0041] As Figures 1-4 shown, Figure 1 This is the schematic cross-sectional structure diagram of the heat flux sensor in this embodiment; Figure 2 This is the three-dimensional schematic diagram of the end view of the heat flux sensor in this embodiment; Figure 3 This is the front view of the measurement end of the heat flux sensor in the embodiment; Figure 4 This is the partial enlarged view of the thermocouple temperature measurement point of the heat flux sensor in this embodiment. The present invention takes CIEM and SMM as the core, and provides a small-sized long-time measurement heat flux sensor based on two algorithms, which can be used in occasions where long-term accurate measurement of the heat flux density or temperature at internal points of an object is required and its continuous change needs to be displayed. The overall shape of the sensor is cylindrical, and it includes five parts: an outer shell 1, a heat insulation material 2, a heat conduction material 3, a first thermocouple 4, and a second thermocouple 5.

[0042] The outer shell 1 is a cylindrical structure with one end open. Inside the outer shell 1, there is a coaxially arranged cylindrical heat conduction material 3. Between the heat conduction material 3 and the outer shell 1, there are the first thermocouple 4 and the second thermocouple 5, and the acquisition ends of the first thermocouple 4 and the second thermocouple 5 are embedded in the heat conduction material 3. The other areas between the heat conduction material 3 and the outer shell 1 are filled with the heat insulation material 2. The first thermocouple 4 and the second thermocouple 5 are exposed thermocouples.

[0043] A first measurement point and a second measurement point are provided on the central axis inside the heat-conducting material 3. The heat-conducting material 3 is respectively provided with a first hole and a second hole along the radial direction at the first measurement point ① and the second measurement point ②. The temperature-measuring filament tips of the first thermocouple 4 and the second thermocouple 5 are respectively embedded in the first hole and the second hole. Adhesive layers are provided in both the first hole and the second hole. The temperature-measuring filament tips of the first thermocouple 4 and the second thermocouple 5 are both wrapped with silicone grease layers, and the silicone grease layers are embedded in the adhesive layers. The top of the temperature-measuring filament tip is in contact with the bottom of the hole; high-viscosity two-phase adhesives are applied to the tops of the first hole and the second hole to reinforce the first thermocouple 4 and the second thermocouple 5.

[0044] The first temperature measurement point can be set within the range of 10 to 20 mm from the measurement end. The purpose is to be relatively close to the measurement end, so as to reflect the heat flux and temperature properties of the measurement end to a greater extent, improve the accuracy of algorithm inversion. It cannot be too close to the measurement end, otherwise the temperature will be too high and the thermocouple has the risk of burning out. It cannot be too far from the measurement end, otherwise it will increase the volume of the heat flux sensor and is not conducive to internal measurement; the second temperature measurement point can be set within the range of 40 to 45 mm. The purpose is to try to widen the distance from the first temperature measurement point as much as possible to allow the heat conduction process to proceed more fully, so as to obtain a greater differentiation between the two sets of temperature data, which is conducive to improving the accuracy of algorithm inversion. It cannot be too close to the measurement end, otherwise the distance from the first temperature measurement point cannot be widened, the differentiation of temperature data is small, and the error is large. It cannot be too far from the measurement end, otherwise it will increase the volume of the heat flux sensor and is not conducive to internal measurement.

[0045] The diameter range of the heat-conducting material 3 is 2.5 to 4 mm. If the diameter is too small, it is not conducive to processing and assembling with the thermocouple. If the diameter is too large, it will cause the overall volume of the heat flux sensor to be too large, and the damage to the object itself during internal measurement by opening holes is relatively large, affecting the use experience; the length range is 45 to 55 mm. If the length is too small, it is not conducive to setting the positions of the two temperature measurement points, and it is difficult to widen the distance to generate temperature differentiation, resulting in a large error. If the length is too long, it will waste materials and also increase the volume of the heat flux sensor, which is not conducive to internal measurement.

[0046] For the selection of the CIEM or SMM algorithm, when the temperature of the measured surface is below 600 °C, the thermal physical properties of the heat-conducting material change linearly with temperature. At this time, the CIEM algorithm should be used to reduce the dependence on the uncertain thermal physical properties and improve the measurement accuracy; when the temperature of the measured surface is 600 °C or above, the thermal physical properties of the heat-conducting material begin to change nonlinearly with temperature. The CIEM algorithm has a poor inversion effect under nonlinear conditions, and using the SMM algorithm for inversion can obtain a more accurate prediction solution.

[0047] Furthermore, the algorithms used in the present invention are all based on a one-dimensional heat transfer model. The heat-conducting material 3 needs to be well insulated, so the heat-insulating material 2 uses HM1800 mullite heat-insulating material, which has excellent heat-insulating performance, but is prone to generating dust after forming. Therefore, a cylindrical shell 1 is designed. The shell 1 is made of stainless steel, with good strength, hardness and corrosion resistance. The heat-conducting material 3 also uses stainless steel material. Due to its good heat-conducting performance, this material can quickly reflect the heat flow situation through temperature changes. At the same time, its thermal resistance is moderate, which will not make the temperature difference between the two measurement points too small, thus avoiding large errors when the algorithm inversely predicts the surface temperature of the measurement. The used first thermocouple 4 and second thermocouple 5 need to meet the following characteristics at the same time: wide temperature measurement range, fast response time, small volume structure, etc. Based on these requirements, the basic specification can select the exposed thermocouple of SCAXL-020E-6.

[0048] In this embodiment, the outer diameter of the cylindrical stainless steel shell 1 is 6 mm, the thickness is 0.5 mm, and the length is 50 mm. The diameter of the heat-conducting material 3 is 3 mm, and the length is about 50 mm. One end is taken as the measurement end and the other end is taken as the tail end. Two temperature measurement points are arranged on the central axis inside the heat-conducting material 3, namely the first measurement point and the second measurement point. At the two temperature measurement points, two holes are respectively arranged along the radial direction. The first measurement point is 15 mm away from the measurement end, and the second measurement point is 40 mm away from the measurement end. The diameter of the hole is about 0.5 mm and the depth is 1.5 mm, that is, to ensure that the temperature measurement points of the thermocouple are at the axis position of the heat-conducting material, making it more in line with the situation of one-dimensional heat transfer.

[0049] The tip parts of the thermocouple wires of the first thermocouple 4 and the second thermocouple 5 are respectively embedded in two holes of the heat-conducting material 3. The specific embedding and fixing operation is as follows: First, use a syringe to pour the high-temperature heat-conducting adhesive Graphi-Bond 669 with good heat conductivity into the holes. After the needle touches the bottom, while pushing the piston to pour the adhesive, move the needle upward until it starts to overflow from the hole opening to ensure that the holes are completely filled with the adhesive and there is no air residue. After removing the part of the glue that overflows from the holes, wrap the tip of the thermocouple wire of the thermocouple with a silicone grease layer, and then insert the tip of the thermocouple wire of the thermocouple into the hole to ensure complete contact with the bottom surface. Wait for about ten minutes. After the filling and bonding layer solidifies, cover a layer of high-viscosity two-phase adhesive CERAMABOND 571 on the hole openings on the surface of the heat-conducting material 3 and let it stand for about one day to completely fix the thermocouples at the first measuring point ① and the second measuring point ②. After fixing the tip parts of the thermocouple wires of the first thermocouple 4 and the second thermocouple 5 to the heat-conducting material 3, place the heat-conducting material 3 in the cylindrical stainless-steel shell 1. The two ends of the heat-conducting material 3 are respectively aligned with the two ends of the shell 1, and the heat-conducting material 3 and the shell 1 are coaxially arranged. The leads of the first thermocouple 4 and the second thermocouple 5 are led along the axis direction between the heat-conducting material 3 and the stainless-steel shell 1 to the tail end of the heat-conducting material 3. There are two openings on the bottom surface of the shell 1. The leads of the first thermocouple 4 and the second thermocouple 5 are led to the outside through the openings, and then the heat-conducting material 3 and the stainless-steel shell 1 are fixed together by welding or integral processing. After the above installation is completed, fill the gap between the heat-conducting material 3 and the stainless-steel shell 1 with powdery HM1800 mullite thermal insulation material 2 and compact it densely. The leads of the first thermocouple 4 and the second thermocouple 5 are wrapped inside by the HM1800 mullite powder.

[0050] The advantages of using the exposed thermocouple in the present invention are as follows:

[0051] Currently, common thermocouples can be divided into three types according to the structural form: exposed type, grounded type, and embedded type, each having its own characteristics and applicable scenarios.

[0052] The measuring end of the exposed thermocouple is directly exposed to the measured environment, and can quickly sense the temperature change. It has good response characteristics for measuring rapidly changing temperatures, can quickly respond to subtle temperature changes, and is suitable for occasions with certain requirements for rapid response such as engine testing. Compared with other types of thermocouples, its structure is simple, without complex insulation or grounding design, and the cost is relatively low;

[0053] The measuring end of the grounded thermocouple is directly welded to the front end of the sleeve. The heat transfer is relatively direct. Its response speed is slower than that of the exposed thermocouple and faster than that of the insulated thermocouple. It can reflect the temperature change relatively quickly, and its structure is relatively simple with good grounding performance. Although grounding can reduce some electrical interference, in an environment with strong electromagnetic interference or electrical noise, its anti-interference ability is still relatively weak, which may affect the measurement accuracy, and its application range is limited and the maintenance requirements are relatively high;

[0054] There is an insulating material layer separating the two measuring ends of the insulated thermocouple, which electrically isolates the measuring part from the measured temperature environment, can effectively reduce the electrical interference between the measuring end and the external environment, improve the accuracy and stability of the measurement, is applicable to occasions with high requirements for measurement accuracy and large electrical interference, has strong anti-interference ability, can be used under various harsh environmental conditions, has a wide application range and a long service life. However, due to the existence of the insulating layer, the speed of heat transfer to the measuring end is relatively slow, resulting in a long response time, and there may be a certain lag for the measurement of rapidly changing temperatures. Its responsiveness is inferior to that of the grounded and exposed types, and its structure is complex and the maintenance cost is high.

[0055] The exposed thermocouple is selected in the present invention. Since the algorithm requires a data set with one-to-one correspondence between temperature and time, in the in-situ measurement, it is necessary to quickly respond to the temperature change of the measuring point, which poses a high requirement for the response speed of the thermocouple. At the same time, since it is necessary to drill holes in the heat-conducting material to bury the thermocouple to the center, in order to minimize the impact on the heat conduction process as much as possible, the volume of the buried part of the thermocouple should be as small as possible. Compared with the grounded and embedded types, the temperature measuring element of the exposed thermocouple is directly exposed in the measured environment, which can minimize the hysteresis phenomenon in the heat transfer process and achieve a rapid response to the temperature change. At the same time, since the temperature measuring thermocouple wire is directly exposed without being coated with insulating material, its volume buried in the heat-conducting material is very small and will not cause great damage to the heat conduction process.

[0056] The advantages of using adhesives in the present invention are as follows: The present invention uses the high-temperature thermal conductive adhesive Graphi-Bond 669 and the highly viscous two-phase adhesive CERAMABOND 571 as adhesives to fix the thermocouple. The high-temperature thermal conductive adhesive Graphi-Bond 669 performs excellently in high-temperature environments due to its excellent thermal stability and chemical corrosion resistance, and can still maintain good performance after long-term use. In addition, this glue has excellent electrical conductivity and thermal conductivity, ensuring stable electrical and thermal performance under high-temperature conditions, thereby reducing the impact of drilling on the internal heat conduction of the heat-conducting material. However, due to its insufficient viscosity, loosening may occur during use. Therefore, it is necessary to use the highly viscous two-phase adhesive CERAMABOND 571 for fixation. CERAMABOND 571 has a strong adhesive force, with a viscosity range of 20,000 to 90,000 cP, which can effectively prevent the problem of thermocouple loosening caused by impact and vibration. This glue is commonly used for the assembly and insulation treatment of ceramic and metal components in high-temperature equipment, meeting the requirements of the present invention for bonding and filling at relatively high temperatures. At the same time, its low thermal conductivity characteristic ensures that it will not have a significant impact on the overall heat transfer when applied to the surface of the hole.

[0057] Before specific gluing, it is necessary to first process the highly viscous two-phase adhesive CERAMABOND 571. CERAMABOND 571 is a composite composed of powder and liquid adhesive, and it needs to be mixed at a mass ratio of powder to liquid of 1.5:1 before use. During operation, slowly add the powder to the liquid and slowly stir using a low-speed stirrer to ensure uniformity. The entire mixing process should be carried out carefully to ensure that all the powder is completely wetted without dry powder lumps, the color remains consistent, and no air bubbles are generated. If the viscosity needs to be adjusted, a diluent (such as 571-T) not exceeding 20% of the total volume can be appropriately added. After sufficient mixing, the ideal state is to obtain a homogeneous paste without obvious stratification or segregation, and its appearance is similar to liquid cement. This adhesive should be used as soon as possible within a short time after preparation to prevent premature hardening. Generally, it is recommended to apply it within 1 to 4 hours after preparation to achieve the optimal performance.

[0058] The specific process of gluing and fixing is as follows: When embedding the thermocouple, considering that it is necessary to minimize the measurement error and response time between the thermocouple and the measurement point as much as possible, we first wrap the tip of the thermocouple wire outside the armored wire of the thermocouple with a silicone grease layer with extremely high heat conduction rate. At the same time, we first inject the high-temperature thermal conductive adhesive Graphi-Bond 669 with good thermal conductivity into the holes of the thermal conductive material. Due to the slender structure of the holes, it is recommended to use a syringe for perfusion. Insert the needle into the hole and slowly inject the glue until it starts to overflow from the hole opening. Then, clean up the excess glue on the surface. Next, insert the tip of the thermocouple into the hole to ensure complete contact with the bottom surface. Wait for about ten minutes. After the filling and bonding layer solidifies, apply a circle of the treated high-viscosity two-phase adhesive CERAMABOND 571 on the outside of the hole opening. Finally, let it stand for about a day to completely fix the thermocouple to the thermal conductive material.

[0059] The advantages of the heat insulation material selected in the present invention are:

[0060] The heat insulation material HM1800 mullite selected in the present invention is a material with excellent heat insulation performance and high temperature resistance. Its thermal conductivity is low, generally about 0.18 - 0.25 W / (m・K), and its heat capacity is small, which can effectively prevent heat transfer and reduce heat storage, with outstanding energy-saving effects; it has excellent high temperature resistance, with a melting point as high as 1810 °C, stable structure at 1500 °C high temperature, low thermal expansion coefficient, and strong thermal shock resistance, and can work stably in high temperature equipment for a long time; it has high mechanical strength, with a compressive strength of up to 5 - 10 MPa or more, good wear resistance, can withstand pressure and impact, and is not easily damaged during use; it has good chemical stability, is resistant to acid, alkali, and salt corrosion and has good oxidation resistance, and can work stably in complex chemical environments; it also has the characteristics of light weight and environmental friendliness, is convenient for handling and installation and is friendly to the environment, is easy to process, can be cut and processed as required, has a long service life, can operate stably in harsh conditions for a long time, reduces the number of repairs and replacements, and reduces costs.

[0061] The usage method of the heat flux sensor is as follows:

[0062] As Figure 5 shown, during measurement, first check whether the instrument is complete. If it is intact and undamaged, connect the leads of the first thermocouple 4 and the second thermocouple 5 to the data acquisition module, record the numbers corresponding to different thermocouple leads, and then connect the bus of the data acquisition module to the computer.

[0063] Before the formal measurement, the user can select the pre-test module through the test platform built in the computer to conduct a pre-test to check whether there are faults in the measurement process. After the pre-test result is normal and error-free, prepare to start the formal measurement.

[0064] Place the measuring end of the heat flux sensor at the position to be measured. If you want to measure the heat flux and temperature on the surface of an object, you can hold the tail end of the sensor and press the measuring end tightly against the surface to be measured. If you want to measure the heat flux and temperature inside an object, you need to first determine the drilling diameter and depth according to the dimensions of the object to be measured and the present invention, and then place the measuring end of the sensor through the drilled hole tightly against the target position and fix it by means of an adhesive or the like. If the position of the sensor in the object to be measured remains stable after a slight external force is applied, it is fully fixed and data acquisition can begin. After installing the sensor, click "Start Measurement" on the test platform, and the system will start continuously recording the temperature data of the first measurement point, i.e., Measurement Point ①, and the second measurement point, i.e., Measurement Point ②, during this period of time until you click "Stop Measurement", at which point the system will terminate the acquisition of temperature data. After the acquisition is completed, you can directly perform inversion prediction for the entire period of time, or select the temperature data of the measurement points for a certain period of time to solve.

[0065] After obtaining the temperature data of the measurement points for a period of time, it is necessary to substitute them into the corresponding algorithm module for calculation and solution. At this time, it is necessary to judge the algorithm to be used according to the temperature of the measurement point. For the selection of the CIEM or SMM algorithm, when the temperature of the measured surface is below 600 °C, the thermal physical properties of the heat-conducting material stainless steel change linearly with temperature. At this time, it is advisable to use the CIEM algorithm to reduce the dependence on the uncertain thermal physical properties and improve the measurement accuracy; when the temperature of the measured surface is 600 °C and above, the thermal physical properties of the heat-conducting material stainless steel begin to change nonlinearly with temperature, and the inversion effect of the CIEM algorithm is poor under non-linear conditions. Using the SMM algorithm for inversion can obtain a more accurate prediction solution.

[0066] If the temperature is higher than 600 °C, then use the SMM algorithm, directly substitute the known thermal physical properties of the heat-conducting material 3, the internal buried positions of the first thermocouple 4 and the second thermocouple 5, and the collected temperature data of the measurement points into the preset program, and finally output the predicted values of the temperature and heat flux density at the measuring end and draw an image of their change with time; if the temperature is lower than 600 °C, then use the CIEM algorithm. First, check the system calibration database to see if there is appropriate data. If there is, directly substitute the calibration data in the database into the program. If not, it is necessary to first conduct a set of calibration experiments at the measured position to obtain reasonable calibration data, and then substitute the calibration data and the collected temperature data of the measurement points into the preset program to calculate and output the predicted values and images of the temperature and heat flux density at the measured position. Finally, the temperature and heat flux density at the measuring end and the images of their changes with time will be clearly displayed on the test platform interface and can also be exported for further analysis.

[0067] After the experiment is completed, first disconnect the computer from the output end of the data acquisition module, then remove the connection between the thermocouple wire and the input end of the data acquisition module, then release the fixation between the sensor and the object to be measured, and finally slowly withdraw the sensor from the measurement point. After simple cleaning and tidying, check whether the sensor is damaged. After checking and ensuring it is okay, let it stand still to dissipate heat for the next use.

[0068] The specific CIEM algorithm is as follows:

[0069] Let \(x\) represent the distance from the test end and \(t\) represent the time.

[0070] Define as the temperature at a distance \(x\) from the test end and at time \(t\);

[0071] as the heat flux density at a distance \(x\) from the test end and at time \(t\).

[0072] The initial condition at \(t = 0\) is

[0073] (1.1)

[0074] where represents the initial ambient temperature.

[0075] Define the temperature and heat flux at \(x = 0\) as

[0076] (1.2)

[0077] (1.3)

[0078] Define the excess temperature as

[0079] (1.4)

[0080] The corresponding initial condition becomes

[0081] (1.5)

[0082] The excess temperature at \(x = 0\) is

[0083] (1.6)

[0084] Then the one-dimensional linear heat conduction equation and heat flux defined by the excess temperature are

[0085] (1.7)

[0086] (1.8)

[0087] where is the thermal diffusivity, is the thermal conductivity.

[0088] Taking the Laplace transform of (1.7), we get

[0089] (1.9)

[0090] Under the zero initial condition (1.5), Equation (1.9) can be transformed into

[0091] (1.10)

[0092] The solution of Equation (1.10) is

[0093] (1.11)

[0094] Taking the derivative of (1.11), the excess temperature gradient is obtained as

[0095] (1.12)

[0096] Then the heat flux after Laplace transform is

[0097] (1.13)

[0098] Let b represent the position of the first measurement point and c represent the position of the second measurement point. Substituting x = b and x = c into (1.11) respectively, we get

[0099] (1.14)

[0100] (1.15)

[0101] where the unknown coefficients A(s) and B(s) are unknown coefficients.

[0102] Performing a transformation on (1.14) and (1.15), the unknown coefficients A(s) and B(s) can be expressed as

[0103] (1.16)

[0104] (1.17)

[0105] Substituting them into (1.11), we get

[0106] (1.18)

[0107] where

[0108] (1.19)

[0109] (1.20)

[0110] At this time, the heat flux can be transformed into

[0111] (1.21)

[0112] Then the excess temperature and heat flux at x = 0 can be expressed as

[0113] (1.22)

[0114] (1.23)

[0115] In order to eliminate and in the two equations, calibration experiments are carried out to obtain the temperatures at two measuring points x = b and x = c, and there are

[0116] (1.24)

[0117] (1.25)

[0118] Assume that b, c, α and the thermocouple characteristics do not change between the two calibration tests, and express and using the calibration temperatures of the two measuring points, and then substitute the results back into formula (1.22) to get

[0119] (1.26)

[0120] Or

[0121] (1.27)

[0122] Using the three-term convolution formula, (1.27) can be finally simplified to

[0123] (1.28)

[0124] Where

[0125] (1.29)

[0126] (1.30)

[0127] It can be seen that and are both completely defined by the calibration data. Therefore, after obtaining the temperatures of the two measuring points through the calibration experiment, the temperature at x = 0 can be solved through formula (1.28).

[0128] Similarly, by using the calibrated temperatures of two measurement points, the and in (1.23) are eliminated, and then through the three-term convolution formula, we can obtain

[0129] (1.31)

[0130] where

[0131] (1.32)

[0132] (1.33)

[0133] Therefore, after obtaining the temperatures of two measurement points through the calibration experiment, the heat flux at x = 0 can also be calculated by equation (1.31).

[0134] The specific SMM algorithm is as follows:

[0135] Let x represent the distance from the test end, and t represent time.

[0136] Define as the temperature at a distance x from the test end and at time t;

[0137] as the heat flux density at a distance x from the test end and at time t.

[0138] The one-dimensional heat conduction equation for non-constant properties is

[0139] (2.1)

[0140] where is the density, is the specific heat capacity.

[0141] The initial condition is

[0142] (2.2)

[0143] where represents the initial ambient temperature.

[0144] Assume that the temperature measured by the thermocouple is the actual temperature at the measurement point. Let represent the position of the first measurement point, and represent the position of the second measurement point. Then we have

[0145] (2.3)

[0146] We respectively obtain and After that, the value of \(x = b\) can be obtained by solving the direct problem 1 the heat flux at .

[0147] Let the spatial node \(x\) and the temporal node \(t\) be represented by indices \(i\) and \(j\) respectively i and \(t\), j the first spatial node with \(i = 0\) is \(x\) 0 \(= b\) 1 the last spatial node with \(i = M\) is \(x\) M \(= 0\). Define the temperature and heat flux at the starting point of the spatial march as

[0148] (2.4)

[0149] (2.5)

[0150] Then we have

[0151] (2.6)

[0152] (2.7)

[0153] where

[0154] (2.8)

[0155] (2.9)

[0156] (2.10)

[0157] (2.11)

[0158] The initial conditions at \(t = 0\) are

[0159] (2.12)

[0160] (2.13)

[0161] With the one-sided boundary conditions (2.5), (2.6) and the initial conditions (2.12), (2.13), the temperature and heat flux at \(x = 0\) can be solved by inverse inversion starting from \(x = b\) 1 .

[0162] The order of this scheme is , currently, due to and not being completely accurate, solving by this method will introduce relatively large errors. For the stability of the results, regularization is also required.

[0163] Therefore, we selected a low-pass Gaussian filter and defined it as

[0164] (2.14)

[0165] where , is the circular cut-off frequency, and is the cut-off frequency.

[0166] Obviously, the cut-off frequency f c defines the regularization parameter. Therefore, the selection of the optimal regularization parameter involves determining the optimal cut-off frequency f c . We determine the optimal cut-off frequency f of the digital filter shown in Equation (2.14) by performing phase-plane and cross-correlation analyses on the heat flux rate n and based on the prediction of the spectra of predefined cut-off frequencies f c (n = 1, 2,..., P).

[0167] The specific algorithm is as follows:

[0168] 1. Filter the raw thermocouple (TC) temperature at x = b 1 and x = b 2 using a Gaussian low-pass filter and fix the cut-off frequency f n according to (2.14).

[0169] 2. Use the filtered TC temperature obtained in step 1 as the boundary condition for the specified direct region and solve the direct heat conduction problem of the spatial domain defined between x = b 1 and x = b 2 . Assume that the sampling frequency is high enough and use the same sampling rate in the finite difference format.

[0170] 3. Evaluate the local heat flux at x = b 1 by control volume. At this point, the temperature 1 and heat flux at x = b have been obtained, providing the one-sided boundary condition for advancing from x = b 1 towards the origin x = 0 in space.

[0171] 4. Filter the TC temperature at x = b 1 in step 1 using the local heat flux density calculated in step 3 and advance towards the x = 0 surface using the above formula to obtain the surface heat flux density and surface temperature.

[0172] 5. For a fixed cut-off frequency, the surface temperature and heat flux density are calculated in step 4. Next, the first derivative of the heat flux is represented by a simple finite difference to obtain the heat flux rate , and then phase plane and cross-correlation analyses are performed.

[0173] 6. For each selected cut-off frequency f n , where n = 1, 2,..., P, steps 1 - 5 are performed and stored for later use in phase plane and cross-correlation analyses. The optimal regularization parameter f n (opt) is determined based on the observed output.

[0174] Simulation results:

[0175] Simulations were performed using stainless steel over a 5 - second period with a data collection frequency of 300 Hz. The optimal regularization parameter was selected through phase plane and cross-correlation analyses as described above, and the results are shown in Figure 7 and Figure 8 . Figure 7 shows the predicted surface heat flux , Figure 8 shows the surface temperature used , where f = 4.0 Hz. Figure 9 Lists the parameters used in the short - time simulation.

[0176] It can be seen that the SMM algorithm has a high accuracy in the inversion prediction solutions of heat flux density and temperature, and the results still maintain good accuracy and stability over a large range of heat flux and temperature, having good application value.

[0177] The above - mentioned specific embodiments are used to explain and illustrate the present invention, rather than to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims of the present invention fall within the protection scope of the present invention.

Claims

1. A small-scale long-term measurement heat flow sensor suitable for two inverse problem algorithms, characterized in that: Used to measure heat flow using a calibration integral equation method and a space propulsion method; the sensor comprises a housing (1), a heat insulating material (2), a heat conducting material (3), a first thermocouple (4) and a second thermocouple (5); the housing (1) is a cylindrical structure with an open end, a coaxially arranged cylindrical heat conducting material (3) is provided inside the housing (1), the first thermocouple (4) and the second thermocouple (5) are provided between the heat conducting material (3) and the housing (1), and the collection ends of the first thermocouple (4) and the second thermocouple (5) are embedded in the heat conducting material (3), and the other area between the heat conducting material (3) and the housing (1) is filled with a heat insulating material (2).

2. A small-scale long-term measurement heat flow sensor suitable for two inverse problem algorithms according to claim 1, characterized in that: The first thermocouple (4) and the second thermocouple (5) are exposed-type thermocouples.

3. A small-sized long-time measurement heat flow sensor suitable for two inverse problem algorithms according to claim 1, characterized in that: A first measuring point and a second measuring point are provided on the inner central axis of the heat conductive material (3), and a first hole and a second hole are provided radially at the first measuring point and the second measuring point, respectively, and the collecting ends of the first thermocouple (4) and the second thermocouple (5) are embedded in the first hole and the second hole, respectively.

4. A small-sized long-time measurement heat flow sensor suitable for two inverse problem algorithms according to claim 3, characterized in that: The collecting ends of the first thermocouple (4) and the second thermocouple (5) are both wrapped with a silicone grease layer and are fixed in the first hole and the second hole respectively by means of an adhesive, wherein the adhesive comprises a thermally conductive adhesive and a high-viscosity two-phase adhesive, the thermally conductive adhesive is poured into the first hole and the second hole, and the high-viscosity two-phase adhesive is coated on the surface of the thermally conductive adhesive to reinforce the first thermocouple (4) and the second thermocouple (5).

5. A small-sized long-time measurement heat flow sensor applicable to two inverse problem algorithms according to claim 3, characterized in that: The thermally conductive material (3) comprises a measuring end and a tail end, the measuring end is aligned with the open end of the housing (1), and the tail end is aligned with the sealed end of the housing (1); the distance between the first measuring point and the measuring end is 10 to 20 mm; and the distance between the second measuring point and the measuring end is 40 to 45 mm.

6. A small-sized long-time measurement heat flow sensor applicable to two inverse problem algorithms according to claim 1, characterized in that: The heat conducting material (3) has a diameter of 2.5-4 mm and a length of 45-55 mm; the heat insulating material (2) has a thickness of 1-1.25 mm; the outer shell (1) has an outer diameter of 5-7 mm, a thickness of 0.5-0.7 mm, and a length of 45-55 mm.

7. A small-sized long-time measurement heat flow sensor suitable for two inverse problem algorithms according to claim 1, characterized in that: The bottom surface of the sealed end of the shell (1) is provided with two openings, and the lead wires of the first thermocouple (4) and the second thermocouple (5) are led between the heat-conducting material (3) and the shell (1) along the axial direction to the tail end of the heat-conducting material (3), and are respectively led to the outside of the shell (1) through the two openings.

8. A small-sized long-time measurement heat flow sensor suitable for two inverse problem algorithms according to claim 1, characterized in that: The heat insulating material (2) is mullite, and the heat conducting material (3) is stainless steel.

9. A small-sized long-time measurement heat flow sensor applicable to two inverse problem algorithms according to claim 1, characterized in that: When the temperature of the measured point is below 600°C, the sensor is suitable for the calibration integral equation method; when the temperature of the measured point is 600°C and above, the sensor is suitable for the space propulsion method.

10. A heat flow measurement method based on two inverse problem algorithms, characterized in that: The method is implemented by using a sensor as claimed in any one of claims 1 to 9, comprising the following steps: The sensor is connected to a data acquisition board to obtain temperature data; the data acquisition board is connected to a computer to transmit the temperature data to the computer; the computer has a built-in calibration integral equation algorithm and a space propulsion algorithm; The algorithm used is selected according to the temperature data of the measured point. When the temperature is below 600 ℃, the calibration integral equation algorithm is selected; when the temperature is above 600 ℃, the space advancement algorithm is selected; after post-processing the temperature data using the selected algorithm, the predicted values ​​of the temperature and heat flux density at the measuring point are output, and an image is generated.

Images