Temperature change distribution measuring sensor

Abstract

Provided is a temperature change distribution measuring sensor for measuring the distribution of in-plane temperature changes.　The present invention includes: a plurality of heat flow sensor elements which are arranged on a substrate surface so as to be separated from each other and which measure heat flux passing in a direction perpendicular to the substrate surface at each arrangement position; and a control processing unit which receives a signal from the heat flow sensor elements and calculates a temperature change distribution in the substrate surface. The substrate surface is a flat surface or a curved surface including an aggregate of known spatial points, and the temperature change distribution in the substrate surface is measured from a single heat source.

Description

Temperature change distribution measurement sensor

[0001] This invention relates to a temperature change distribution measuring sensor that measures the distribution of temperature changes within a plane.

[0002] A multi-point temperature sensor is known that determines the temperature distribution within a plane by measuring the temperature at multiple points within that plane. Such a sensor measures the temperature at each temperature sensor element placed within the plane to obtain the temperature distribution within that plane. However, a temperature sensor cannot measure the correct temperature until the heat emitted from the heat source reaches the heat capacity of the temperature sensor itself. Therefore, with a multi-point temperature sensor, even if the temperature change at a certain point (position of the temperature sensor element) over a predetermined time is measured as zero, it is not possible to distinguish whether the data is missing or whether the temperature change is actually zero. Furthermore, since temperature measurement alone cannot distinguish between endothermic and exothermic processes, it is not possible to distinguish the direction of heat passing through the plane, and therefore the distribution of temperature change cannot be measured.

[0003] On the other hand, heat flow sensors, which measure the heat flow (heat flux) that causes temperature changes, are also known. Because they directly detect the heat flow, they can measure temperature changes within a plane more quickly and accurately than temperature sensors.

[0004] For example, Patent Document 1 discloses a heat flow sensor for detecting the heat source distribution beneath biological skin as a wristwatch-type wearable device. It states that by arranging multiple heat transfer elements in a planar manner and interposing an insulating material between adjacent heat transfer elements, a temperature difference is created between each heat transfer element perpendicular to the surface, which is then detected, allowing the heat flow distribution of the measurement surface to be measured. Such a detection method may use thermocouples, thermistors, platinum resistance thermometers, and other temperature measuring elements.

[0005] Japanese Patent Publication No. 2017-131541

[0006] A temperature change distribution measuring sensor, which measures the distribution of temperature changes within a plane, can measure the distribution of heat inflow and outflow, the direction and amount of heat passing through each point, etc., which were not possible with a temperature sensor.

[0007] The present invention has been made in view of the above circumstances, and its objective is to provide a temperature change distribution measuring sensor that measures the distribution of temperature changes within a plane.

[0008] The temperature change distribution measuring sensor according to the present invention is a temperature change distribution measuring sensor that measures the distribution of temperature change in a plane, and includes a plurality of heat flow sensor elements arranged spaced apart from each other on a substrate surface and measuring the heat flux passing perpendicular to the substrate surface at each arrangement position, and a control processing unit that receives signals from the heat flow sensor elements and calculates the temperature change distribution in the substrate surface, wherein the substrate surface is a plane or curved surface consisting of a collection of known spatial points, and provides measurement of the temperature change distribution in the substrate surface from a single heat source.

[0009] According to these features, it is possible to measure the distribution of temperature changes within the substrate surface on which the heat flow sensor element is placed.

[0010] In the above-described invention, the control processing unit may be characterized by smoothing the signal from the heat flow sensor element in accordance with the shape of the substrate surface and calculating the heat flux. According to this feature, changes in the temperature distribution within the substrate surface can be accurately measured.

[0011] The above-described invention may be characterized by including at least one temperature measuring section on the substrate surface. The temperature measuring section may also be characterized by being at least one of the heat flow sensor elements, and by being equipped with a temperature measuring capability. The temperature measuring section may also be characterized by being a thermistor-type heat flow sensor element that measures the temperature difference measured by two thermistors spaced perpendicular to the substrate surface. With such features, it is possible to measure the temperature distribution in addition to the temperature change distribution.

[0012] In the above-described invention, the control processing unit may be characterized by performing cross-analysis of the heat flux measured at the spatial positions of the heat flow sensor elements to calculate the spatial position of a single heat source. The control processing unit may also be characterized by calculating the amount of heat generated by the heat source from the heat flux at each of the heat flow sensor elements. The control processing unit may also be characterized by estimating the in-plane diffusion and mutual interference between the heat flow sensor elements and correcting the heat flux at each of the heat flow sensor elements. The control processing unit may also be characterized by providing a prediction of the amount of heat generated from the time change of the amount of heat generated. According to these features, in addition to the distribution of temperature changes, the position of the heat source and its amount of heat generated can be estimated, and furthermore, the amount of heat generated can be predicted.

[0013] In the above-described invention, the control processing unit may be characterized by providing a prediction of the temperature in the plane based on the total heat quantity obtained from the sum of the heat flux and the temperature change in the plane at predetermined time intervals. With this feature, in addition to the distribution of temperature changes, it is possible to predict the future temperature in the plane.

[0014] This is a plan view of a temperature change distribution measurement sensor. This is a block diagram of a heat flow measurement device using a temperature change distribution measurement sensor. This is a diagram explaining the principle of estimating the location of a heat source. This is a flowchart of the inverse problem solution method for estimating the location of a heat source and the amount of heat generated. This is a diagram of a simplified thermal circuit model. This is a flowchart showing the operation of a temperature prediction system using a temperature change distribution measurement sensor.

[0015] Hereinafter, embodiments of the temperature change distribution measuring sensor according to the present invention will be described with reference to Figures 1 to 6. In each figure, the scale of each component has been appropriately changed for explanatory purposes in order to make each component recognizable. Also, the same or equivalent parts are denoted by the same reference numeral, and redundant explanations are omitted.

[0016] [First Embodiment] (Structure of Temperature Change Distribution Measurement Sensor 1) The first embodiment will be described with reference to Figures 1 to 5. Figure 1 is a plan view showing two arrangements of heat flow sensor elements for the temperature change distribution measurement sensor 1 according to the first embodiment, Figure 2 is a block diagram showing the schematic functional configuration of a heat flow measurement device 10 using the temperature change distribution measurement sensor 1, Figure 3 is a diagram explaining the principle of estimating the position of the heat source, Figure 4 is a flowchart of the inverse problem solution method for estimating the position of the heat source and the amount of heat generated, and Figure 5 is a diagram of a simplified thermal circuit model.

[0017] As shown in Figure 1, the temperature change distribution measurement sensor 1 has multiple heat flow sensor elements 2 arranged on a substrate 3 at intervals from each other to measure heat flux. Each of the heat flow sensor elements 2 is electrically connected to the outside of the substrate 3 by wiring patterns 4 formed on the substrate 3, and the measured value as a heat flow sensor can be calculated by a control processing unit described later. Connectors 41 are provided at the ends of the wiring patterns 4 to serve as connection points to the outside.

[0018] The material of the substrate 3 is not particularly limited, but it is preferable to use a sheet-like substrate made of a flexible material. For example, an FPC substrate can be suitably used. Furthermore, it is preferable that the substrate 3 be made of a material with low thermal conductivity in order to reduce heat conduction in the direction along the main surface.

[0019] The heat flow sensor element 2 measures the heat flux perpendicular to the substrate surface at each placement position on the substrate surface, which is the main surface of the substrate 3. The type of heat flow sensor element 2 is not particularly limited, but examples include Peltier element type heat flow sensor, thermocouple type heat flow sensor, thermistor type heat flow sensor, etc.

[0020] In particular, when a thermistor-type heat flow sensor element 2, which has temperature measurement capabilities that can measure not only heat flux but also temperature, is applied, it is possible to obtain information on heat flow and temperature simultaneously. The thermistor-type heat flow sensor has two thermistors placed at two locations on the substrate surface of the substrate 3 and at a position perpendicularly separated from it, and the heat flux can be calculated by measuring the temperature difference measured by these two thermistors.

[0021] In particular, in Fig. (a), a plurality of straight lines Lh and Lv are arranged at equal intervals vertically and horizontally, and the heat flux sensor elements 2 are arranged on the intersections thereof. That is, the heat flux sensor elements 2 are arranged in a grid pattern. At this time, two of the heat flux sensor elements 2 are paired and arranged at the symmetric positions of the virtual line V at the center of each line segment, respectively.

[0022] In Fig. (b), the heat flux sensor elements 2 are arranged at the intersections of the circumferences of the concentric circles CC around the center point P and a plurality of straight lines D passing through the center point P which is a virtual point, and on the center point P. That is, except for the heat flux sensor element 2 on the center point P, two of the heat flux sensor elements 2 are paired and arranged at the symmetric positions of the center point P, respectively. Here, the concentric circles are a plurality of circles with the same difference in radius, and the straight lines D are arranged at equal angular intervals.

[0023] By arranging them at such symmetric positions, it becomes easy to specify the respective spatial positions of the heat flux sensor elements 2. Further, by arranging the heat flux sensor elements 2 over a wide range, the temperature change distribution can be comprehensively measured. Here, it is assumed that each of the heat flux sensor elements 2 is spaced apart so that the influence of heat conduction in the direction along the main surface of the substrate 3 from the position of the adjacent heat flux sensor element 2 can be ignored. As a result, each of the heat flux sensor elements 2 can independently measure the heat flux without being affected by heat conduction in the direction along the main surface of the substrate 3. As a result, the temperature change distribution measurement sensor 1 can accurately measure the distribution of heat inflow and outflow. Regarding the in-plane diffusion of heat and the mutual interference of the heat flux sensor elements due to heat conduction in the direction along the main surface remaining even by the above, it can be further reduced by using the combined use of the above-described spaced arrangement of the heat flux sensor elements and the compensation algorithm described later.

[0024] (Control Processing Unit 20) As shown in Fig. 2, the heat flux measurement device 10 has a control processing unit 20 that independently controls the signals of each of the heat flux sensor elements 2 of the temperature change distribution measurement sensor 1. The control processing unit 20 is connected to each of the heat flux sensor elements 2 via the connector 41. Each of the heat flux sensor elements 2a, 2b, 2c... calculates the heat flux by each of the heat flux measurement units 30a, 30b, 30c..., and the heat flux distribution of the object to be measured can be obtained.

[0025] The control processing unit 20 further includes a heat flow direction intersection calculation unit 40 and a heat source position estimation unit 50. By means of these units, as will be described later, an intersection analysis can be performed to estimate the position Q of the heat source by analyzing the intersection position of the direction of the heat flux vector from the spatial positions of each heat flux sensor element 2 and the value of each heat flux.

[0026] Specifically, as shown in FIG. 3, the direction perpendicular to the main surface of the substrate 3 on which the heat flux sensor elements 2 are arranged is the normal direction of the main surface of each heat flux sensor element 2. Here, since the measured value of each heat flux sensor element 2 arranged at each point is the heat flux penetrating in the normal direction of each element, it measures the normal direction component of the heat flux in the actual heat flow direction (the radiation direction from the heat source) at the position where the heat flux sensor element 2 is arranged. Hereinafter, the heat flux in the actual heat flow direction at the position of each heat flux sensor element 2 will be referred to as a heat flux vector, and its normal direction component will be referred to as a heat flux (normal component) vector. In this embodiment, the heat source Q is a single point heat source, and the thermal conductivity is constant regardless of the position in the space from the heat source Q to the temperature change distribution measurement sensor 1, and a steady heat flow is handled.

[0027] The magnitude of the measured heat flux (normal component) vector varies depending on the distance from the heat source Q. That is, if the distance from the heat source Q is close, the heat flux (normal component) vector becomes large, and if the distance from the heat source Q is far, the heat flux (normal component) vector becomes small. The magnitude is such that a line perpendicular to the main surface of the substrate 3 is L, and the heat flux vector at each spatial position x of each heat flux sensor element 2 is q(x ｉ ), the normal vector of the main surface of each heat flux sensor element 2 is n ｉ , and when the heat flux density of the normal component measured by each heat flux sensor element 2 is g ｉ , it is g ｉ = q(x ｉ ) · n ｉ . And the voltage output of each heat flux sensor element becomes y shown in the following (Equation 1) ｉ (V). Here, k is a proportionality constant based on the relationship between the heat flux and the voltage output (depending on the sensitivity of the sensor and the intensity of the heat flux), θ ｉis the angle between the heat flow direction (the direction of the heat flow vector) and the normal to the main surface of the substrate at the spatial position of each heat flow sensor element. Thus, the voltage output y ｉ is represented by a function proportional to cosθ ｉ , and thus the angle θ ｉ can be obtained from the output values of each heat flow sensor. That is, the heat flow direction can be estimated from the angle θ ｉ , and the position of the intersection of the straight lines extending in the direction of the angle θ ｉ from the spatial positions of each heat flow sensor element can be analyzed and calculated, and estimated as the spatial position of the heat source.

[0028] In this way, the spatial position of the heat source Q can be estimated by geometric analysis. Note that even if the substrate 3 is a curved surface, the spatial position of the heat source can be calculated in the same manner if the spatial position and the normal direction of each heat flow sensor element 2 are specified. That is, the substrate surface may be a plane or a curved surface composed of a set of known spatial points.

[0029] Furthermore, the estimation accuracy of the position of the heat source can also be improved by using a hypersurface passing through the spatial positions of each heat flow sensor element 2. Here, assuming that (x1, x2... xn) are the coordinates indicating the spatial positions of the heat flow sensor elements 2, a hypersurface passing through the spatial positions of each heat flow sensor element 2 is assumed by the following (Equation 2). Next, as shown in (Equation 3), a gradient vector can be obtained by partially differentiating the hypersurface at the spatial positions of each heat flow sensor element 2. Then, the normal vector of the hypersurface at the spatial position of the heat flow sensor element 2 can be obtained from this gradient vector. Using the matching condition with the measured y ｉ (θ ｉ ), smoothing processing of the direction field is performed, and the robustness in the analysis of the intersection position of the heat flow directions can be enhanced. Note that such smoothing processing can accurately obtain the temperature distribution change within the substrate surface.

[0030] Furthermore, it is preferable that the substrate surface of the substrate 3 undergoes selective shape changes. For example, if the substrate 3 can be made into a plate-like body that undergoes selective shape changes such as being planar and curving in a specific direction with a specific curvature, then the spatial position of the heat flow sensor element 2 can be easily calculated by detecting these two types of shape changes, and the position of the heat source can be estimated as described above.

[0031] Furthermore, it is preferable that the control processing unit 20 also includes a heat generation estimation unit 60, which is a program that calculates the amount of heat generated by the heat source Q whose location has been estimated from the heat flux, and further predicts the amount of heat generated thereafter from the time change of the amount of heat generated.

[0032] As shown in Figure 4, it is also preferable that the control processing unit 20 includes an inverse problem solving program 100 that estimates the location and amount of heat generated by the heat source of the object being measured based on steady-state heat flux distribution data obtained from multiple heat flow sensor elements 2 of the temperature change distribution measurement sensor 1. This makes it possible to estimate the amount of heat generated by the heat source Q.

[0033] The inverse problem solving program 100 includes the processes of forward problem analysis, likelihood evaluation, Bayesian estimation (Metropolis-Hastings method), and statistical calculation, and operates in the following procedure.

[0034] (1) Acquisition of heat flow distribution (S101) First, the measured value g of steady-state heat flow density at multiple locations on the surface of the object to be measured (each placement point of the heat flow sensor element 2) as defined by (Equation 4) below. ｏｂｓ This will be obtained as observational data. Here, g ｉ ｏｂｓ x is the heat flow density of the normal component of each heat flow sensor element 2, and N is the number of sensor elements. The spatial position (coordinates) of each sensor is x ｉ This is obtained by identifying the shape state of the substrate 3 (selective shape change between planar and curved as described above).

[0035] (2) Model setting (S102) In model setting, the heat conduction inside the object to be measured is modeled using a three-dimensional steady-state heat conduction equation. That is, The governing equations are given by , and Dirichlet boundary conditions (temperature known) or Neumann boundary conditions (heat flow known) are set for the boundary Γ. Here, k(x) is the thermal conductivity, T(x) is the temperature field consisting of the temperature at position x, and q(x) is the internal heat source density.

[0036] (3) Definition of heat source parameters (S103) In the definition of heat source parameters, the heat source inside the body being measured is parameterized as a set of multiple point heat sources. That is, That is. Furthermore, Γ ｍ Q is the position vector indicating the spatial position of the mth heat source (point heat source). ｍ is the heat generated ([W]). M is determined as needed by known values ​​or model selection.

[0037] (4) Forward problem analysis (S104) In forward problem analysis, the above governing equation is solved for the given candidate parameter θ to obtain the temperature field T(x|θ). Next, the sensor position x ｉ Theoretical heat flux density in the normal direction The theoretical heat flow distribution is calculated. Outputs n i This is the normal vector to the main surface of each heat flow sensor element 2.

[0038] (5) Likelihood evaluation (S105) In the likelihood evaluation, the measured value g of the observed steady-state heat flux is used. ｏｂｓ Based on the difference between the theoretical heat flow distribution g(θ) and the observed noise, the Gaussian noise N(0,σ) is determined to be independent and homovariant. ２ ) Assuming the likelihood Calculate σ as needed. ２ This is determined by pre-evaluation or learning.

[0039] (6) Bayesian estimation (S106) In Bayesian estimation, first, a prior distribution is set for the spatial position and heat generation of the point heat source. For example, the position vector Γ of the point heat source ｍ The prior distribution Assume that the position is uniformly distributed within the target region D, and the heat generation is a semi-normal distribution with a non-negative constraint. The posterior distribution in this case is: This is the result.

[0040] Based on the Metropolis-Hastings method (MH method), a new candidate θ' is generated from the proposal distribution q(θ'|θ), and the acceptance probability is... The function θ' is accepted or rejected based on this. This is repeated a predetermined number of times or until the convergence condition (e.g., the change in the moving average of the posterior log probability is less than or equal to a threshold) is met.

[0041] (7) Calculation of post-hoc statistics (S107) The statistics calculation unit calculates the obtained sample set Based on this, the following is calculated: • Post-hoc average of the position vectors of each point heat source: E[Γ] ｍ ] • Estimated maximum posterior probability (MAP): θ ＭＡＰ - The spatial location and confidence interval (e.g., 95% CI) of the heat source Q. - Clustering of multiple solutions, note that θ ＭＡＰ The following applies: Furthermore, in "clustering with multiple solutions," if there are multiple modes in the sample, the position vector Γ ｍ Clustering of the set (e.g., distance-based hierarchical clustering) outputs multiple candidate point heat sources.

[0042] The estimated position vector of the point heat source obtained by Bayesian estimation (position and confidence interval) is passed to the heat flow direction intersection calculation unit 40 and the heat source position estimation unit 50, and by integrating it with the results of the geometrical cross-analysis described above, the consistency of the estimation can be verified and outliers can be suppressed. Furthermore, the time change of the estimated heat generation amount (during sequential operation) is passed to the heat generation amount estimation unit 60, and by combining it with the compensated measured values ​​of the simplified thermal circuit model described later, it can also be used for future temperature prediction.

[0043] The inverse problem solving program 100 allows users to select between a simplified Green's function approximation or a low-order finite element approximation for the forward problem solving method. Furthermore, by using adaptive covariance (Adaptive MH) in addition to a random walk without gradient as the proposal distribution, it achieves a low computational load suitable for real-time estimation. In other words, it becomes easy to use for automotive and wearable applications.

[0044] The control processing unit 20 also preferably has a compensation algorithm that uses a model to compensate for in-plane thermal diffusion and mutual interference between sensors, based on measurements obtained from a plurality of heat flow sensor elements 2 arranged on the substrate 3.

[0045] For example, a simplified thermal circuit model like the one shown in Figure 5 can be used. In this simplified thermal circuit model, the position of each heat flow sensor element 2 is defined as node N. ｉ Let any two nodes N ｉ、 N ｊ The thermal resistance R represents the heat conduction within the substrate surface. ij Assuming that each node N ｉ to reference temperature (ambient temperature) T ａｍｂ By connecting a heat capacity Ci representing local heat storage toward this point, the heat conduction within the plane can be approximately represented.

[0046] Specifically, each node N １、 and N ２ The measured values ​​of the heat flow sensor element 2 corresponding to each node N are input, and the adjacent node N １、 N ２ Thermal resistance R represents the heat conduction between them. １２ Set the thermal resistance value R. １２ This is determined based on the material, thickness, and distance between heat flow sensor elements of the substrate 3. Also, each node N １ and N ２ Each has a heat capacity C １ and C ２ This model incorporates a mechanism to model thermal diffusion over time. This model allows for the estimation of interference caused by heat conduction along the main surface of the substrate between adjacent sensors, and enables correction of measured values.

[0047] The compensation algorithm is executed in the following steps: (1) Obtain the spatial position (coordinates) of the heat flow sensor element. (2) Generate a thermal resistance matrix between each node. (3) Input the measured values ​​and run a simulation based on the thermal circuit model. (4) Calculate a correction coefficient from the difference between the simulation results and the measured values. (5) Output the corrected heat flux value.

[0048] In addition to the simplified thermal circuit model, other compensation models such as the finite element method model and the Green's function model using the solution to the heat conduction equation can be used. The simplified thermal circuit model has a lower computational load compared to detailed analysis using the finite element method and is advantageous for real-time compensation processing, making it suitable for applications such as wearable devices and automotive sensors.

[0049] [Second Embodiment] A second embodiment will be described with reference to Figure 6. Figure 6 is a flowchart showing the operation of a temperature prediction system using the temperature change distribution measurement sensor 1 according to the second embodiment.

[0050] The temperature prediction system is a system that predicts the future temperature of an object being measured by further equipping a temperature change distribution measurement sensor 1 with a temperature measurement unit. The temperature measurement unit may use an element that has the function of measuring temperature as a heat flow sensor element 2.

[0051] Specifically, the method includes the steps of: measuring the heat flux of each heat flow sensor element 2 of the temperature change distribution measuring sensor 1 placed on the surface of the object to be measured (S1); calculating the total heat quantity of the object to be measured from the sum of the heat fluxes (S2); measuring the temperature of the object to be measured at predetermined time intervals (S3); and predicting the future temperature of the object to be measured (S4).

[0052] The temperature prediction system can predict future temperature anomalies in the object being measured, making it possible to take action before an anomaly occurs. The temperature prediction system may be implemented as a program in the control processing unit 20 of the heat flow measuring device 10 shown in the second embodiment.

[0053] As described above, these embodiments allow for the measurement of the heat flux distribution on the surface of an object under test. Furthermore, since the location of the heat source and the amount of heat generated by the heat source can be estimated from the intersection of the heat flux values ​​and the heat flow direction, it is possible to estimate the location of the heat source and the amount of heat generated in objects under test, such as batteries, motors, and the human body, where internal heat flow measurement is not easy. This makes it possible to identify abnormal areas in the object under test. In addition, if a temperature change distribution measurement sensor 1 is used as the heat flow sensor element 2, which has a temperature measurement capability that enables temperature measurement, the amount of heat and temperature passing through the temperature change distribution measurement sensor 1 can be measured simultaneously, making subsequent temperature prediction possible.

[0054] For example, when the object being measured is the human body, metabolic heat flow can be tracked to monitor the need for temperature regulation and hydration, and to understand the level of heat stress, thereby preventing overheating, dehydration, and heatstroke. Furthermore, by using it on components that come into contact with human tissue, such as prosthetics and medical implants, heat transfer can be measured and applied to the design of components that can appropriately regulate temperature. This can improve comfort by preventing overheating and cooling during the use of these components, thereby improving quality of life (QoL).

[0055] Furthermore, by using it in devices that generate heat, such as electronic equipment and batteries, it is possible to actively manage heat generation and dissipation, monitor thermal load, prevent overheating of the equipment, optimize energy efficiency, and even improve the safety and performance of the equipment.

[0056] Furthermore, it can be used to monitor heat flow through building materials, which can help reduce heating and cooling costs and improve energy efficiency.

[0057] It can also be applied to the design of cooking equipment such as industrial ovens, grills, or frying systems to improve performance, ensuring even heating during cooking, reducing energy consumption, and preventing undercooking or overcooking. Using high-performance cooking equipment can also improve the quality of the food being cooked.

[0058] By measuring the temperature change distribution on the reactor surface, continuously monitoring heat dissipation and thermal gradients, and detecting anomalies, it is possible to prevent thermal runaway and reactor damage, thereby improving the safety and reliability of the reactor.

[0059] In metal manufacturing, measuring the temperature change distribution in containers of high-temperature materials such as molds can be applied to precisely control the cooling rate in the solidification process, and further contribute to improving energy efficiency and the quality of manufactured metal products.

[0060] Although embodiments and modifications based thereon have been described, the present invention is not necessarily limited to these examples. Furthermore, those skilled in the art will be able to find various alternative embodiments and modifications without departing from the spirit of the present invention or the scope of the attached claims.

[0061] 1. Temperature change distribution measurement sensor 2. Heat flow sensor element 3. Circuit board 4. Wiring pattern

Claims

1. A temperature change distribution measuring sensor for measuring the distribution of temperature changes within a surface, comprising: a plurality of heat flow sensor elements arranged spaced apart on a substrate surface and measuring the heat flux passing perpendicular to the substrate surface at each arrangement position; and a control processing unit that receives signals from the heat flow sensor elements and calculates the temperature change distribution within the substrate surface, wherein the substrate surface is a plane or curved surface consisting of a collection of known spatial points, and the sensor provides measurement of the temperature change distribution within the substrate surface from a single heat source.

2. The temperature change distribution measuring sensor according to claim 1, characterized in that the control processing unit smooths the signal from the heat flow sensor element to correspond to the shape of the substrate surface and calculates the heat flux.

3. The temperature change distribution measuring sensor according to claim 1 or 2, characterized in that it includes a temperature measuring unit that measures the temperature at least one location on the substrate surface.

4. The temperature change distribution measuring sensor according to claim 3, characterized in that the temperature measuring unit is at least one of the heat flow sensor elements, and the heat flow sensor element is provided with a temperature measuring capability.

5. The temperature change distribution measuring sensor according to claim 4, characterized in that the temperature measuring unit is a thermistor-type heat flow sensor element that measures the temperature difference measured by two thermistors spaced perpendicularly to the substrate surface.

6. The temperature change distribution measuring sensor according to one of claims 1 to 5, characterized in that the control processing unit cross-analyzes the spatial position of the heat flow sensor element and the heat flux measured at the spatial position to calculate the spatial position of a single heat source.

7. The temperature change distribution measuring sensor according to claim 6, characterized in that the control processing unit calculates the amount of heat generated by the heat source from the heat flux at each of the heat flow sensor elements.

8. The temperature change distribution measuring sensor according to claim 7, characterized in that the control processing unit estimates the in-plane diffusion and mutual interference between the heat flow sensor elements and corrects the heat flux at each of the heat flow sensor elements.

9. The temperature change distribution measuring sensor according to claim 7, characterized in that the control processing unit provides a prediction of the amount of heat generated from the time change of the amount of heat generated.

10. The temperature change distribution measuring sensor according to claim 9, characterized in that the control processing unit provides a prediction of the temperature in the plane based on the total heat amount obtained from the sum of the heat flux and the temperature change in the plane at predetermined time intervals.

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