Abnormality detection method and abnormality detection device

Abstract

To provide an abnormality detection method and an abnormality detection device that can theoretically calculate a physical quantity that is difficult to read with a sensor.SOLUTION: An abnormality detection method comprises a step of inputting known data including information acquired by a sensor provided for a heat exchanger, into a numerical analysis model using a thermal network method of the heat exchanger that circulates a medium to exchange heat with an outside, and deriving first unknown data related to temperature inside the heat exchanger and second unknown data related to a flow rate of the medium, and a step of comparing the second unknown data with a predetermined threshold value to detect an abnormality in the heat exchanger.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present embodiment relates to an abnormality detection method and an abnormality detection device. [Background technology]

[0002] Diagnosing deterioration and abnormalities in train air conditioning systems often involves a method (direct diagnosis) that uses threshold processing to identify abnormalities based on physical quantities (temperature, pressure, etc.) read by sensors. Ordinary direct diagnosis has the problem of being unable to identify abnormalities related to physical quantities (flow rate, heat transfer coefficient, etc.) that are difficult to read with sensors. It is sometimes possible to calculate these physical quantities based on the physical conditions that the air conditioning equipment must comply with. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-252659 [Non-patent literature]

[0004] [Non-Patent Document 1] "Prediction of heat exchanger performance for air conditioning using thermal network method", Kaga et al., 2000. Summary of the Invention [Problem to be solved by the invention]

[0005] For example, a method known as the thermal network method has been proposed for analyzing heat transfer in air conditioning equipment. This method considers heat flow to be similar to the flow of electricity and calculates heat transfer using the same calculation method as an electrical circuit. A method has also been invented for calculating the temperature distribution and performance of a heat exchanger using this thermal network method. However, conventional methods are limited to analyzing the temperature distribution within a heat exchanger using the thermal network method, and no method has been proposed for estimating physical quantities other than temperature, such as flow rate and heat transfer coefficient.

[0006] Additionally, a method for detecting anomalies in air conditioning equipment using machine learning has also been proposed, but the problem with anomaly diagnosis using machine learning (indirect diagnosis) is that it has weak detection power in the extrapolated region of the training data (unknown conditions).In addition, because the calculation process using machine learning is generally a black box, even if machine learning methods can detect anomalies, they are not suitable for theoretically explaining why an anomaly occurred.

[0007] Therefore, one embodiment of the present invention provides an anomaly detection method and an anomaly detection device that can theoretically calculate physical quantities that are difficult to read with sensors. [Means for solving the problem]

[0008] According to this embodiment, the method includes the steps of inputting known data, including information acquired by sensors provided for the heat exchanger, into a numerical analysis model based on a thermal network method of the heat exchanger that circulates a medium and exchanges heat with the outside, to derive first unknown data related to the temperature inside the heat exchanger and second unknown data related to the flow rate of the medium; and a procedure for comparing the second unknown data with a predetermined threshold to detect an abnormality in the heat exchanger. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a conceptual diagram of a refrigeration cycle to which an abnormality detection method according to a first embodiment of the present disclosure can be applied. [Figure 2A] 1 is a schematic diagram of a piping structure according to a first configuration example of a heat exchanger according to a first embodiment of the present disclosure. [Figure 2B] FIG. 2B is a cross-sectional view of the piping structure of FIG. 2A. [Figure 3A] FIG. 2 is a diagram illustrating a divided cell configuration of a first configuration example of a numerical analysis model according to a first embodiment of the present disclosure. [Figure 3B] FIG. 2 is a diagram illustrating a thermal circuit of a first configuration example of a numerical analysis model according to a first embodiment of the present disclosure. [Figure 4A] FIG. 2 is a diagram illustrating a governing equation for a metal-metal cell according to the first embodiment of the present disclosure. [Figure 4B] FIG. 2 is a diagram illustrating a governing equation for a metal cell according to the first embodiment of the present disclosure. [Figure 4C] FIG. 2 is a diagram illustrating a governing equation for a refrigerant cell according to the first embodiment of the present disclosure. [Figure 5] 5 is a flowchart showing a procedure for outputting a temperature distribution according to the first embodiment of the present disclosure. [Figure 6A] FIG. 10 is a diagram showing the temperature distribution of the heat exchanger according to the first configuration example, derived by the temperature distribution output procedure according to the first embodiment of the present disclosure. [Figure 6B] FIG. 10 is a diagram showing the temperature distribution of the heat exchanger according to the first configuration example, which is derived from a differential equation according to a comparative example. [Figure 7A] FIG. 4 is a schematic diagram of a piping structure according to a second configuration example of the heat exchanger according to the first embodiment of the present disclosure. [Figure 7B] FIG. 7B is a one-dimensional representation of the heat exchanger of FIG. 7A. [Figure 7C] FIG. 4 is a diagram illustrating a second configuration example of the numerical analysis model according to the first embodiment of the present disclosure. [Figure 8A] FIG. 2 is a diagram illustrating a governing equation in a fin cell according to the first embodiment of the present disclosure. [Figure 8B] FIG. 2 is a diagram illustrating a governing equation in a tube cell according to the first embodiment of the present disclosure. [Figure 9A] FIG. 10 is a diagram showing the temperature distribution of a heat exchanger according to a second configuration example derived by the temperature distribution output procedure according to the first embodiment of the present disclosure. [Figure 9B] FIG. 10 is a diagram showing the temperature distribution of the heat exchanger according to the second configuration example, derived from a model according to a comparative example. [Figure 10] FIG. 4 is a waveform diagram showing the relationship between the number of calculations and the error according to the brute force search method according to the first embodiment of the present disclosure. [Figure 11] 1 is a block diagram showing an abnormality detection device to which an abnormality detection method according to a first embodiment of the present disclosure is applied. [Figure 12] FIG. 10 is a diagram showing state transitions of a refrigerant in a heat exchanger according to a second embodiment of the present disclosure. [Figure 13] 10 is a flowchart showing a procedure for outputting a temperature distribution according to a second embodiment of the present disclosure. [Figure 14] FIG. 10 is a waveform diagram showing the relationship between the temperature and specific heat of two adjacent refrigerant cells according to a second embodiment of the present disclosure. [Figure 15A] FIG. 10 is a diagram showing a data set for dryness fraction according to a second embodiment of the present disclosure. [Figure 15B] FIG. 10 is a diagram illustrating a data set for density according to a second embodiment of the present disclosure. [Figure 15C] FIG. 10 shows a data set for specific enthalpy according to a second embodiment of the present disclosure. [Figure 16A] FIG. 10 is a first diagram illustrating an interpolation method for physical property values ​​according to a second embodiment of the present disclosure. [Figure 16B] FIG. 2 is a second diagram illustrating an interpolation method for physical property values ​​according to the second embodiment of the present disclosure. [Figure 17] FIG. 10 is a diagram showing the amount of enthalpy across a two-phase region according to a second embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] (First embodiment) Fig. 1 is a conceptual diagram of a refrigeration cycle to which the anomaly detection method according to the first embodiment of the present disclosure can be applied. The air conditioning equipment 1 shown in Fig. 1 has an evaporator (first heat exchanger) 2, a compressor 3, a condenser (second heat exchanger) 4, and an expansion valve 5. The air conditioning equipment 1 can be used for a variety of purposes, but the following describes an example in which it is used as an air conditioner for a train.

[0012] The evaporator 2 and the condenser 4 exchange heat between two fluids, such as gases or liquids, with different temperatures, for example, between the refrigerant flowing through the piping and the air surrounding the piping. The piping structures of the evaporator 2 and the condenser 4 will be described later.

[0013] The compressor 3 compresses the refrigerant to raise its temperature, and the expansion valve 5 reduces the pressure and temperature of the refrigerant.

[0014] The operation of the air conditioning equipment 1 is explained below. First, an evaporator 2 located inside the vehicle evaporates a liquid refrigerant and cools the air inside the vehicle by absorbing heat. The gaseous refrigerant is compressed by a compressor 3 to a high temperature and supplied to a condenser 4. The condenser 4 located outside the vehicle condenses and liquefies the refrigerant, and releases the heat of the refrigerant outside the vehicle by heat dissipation. The high-pressure liquid refrigerant is returned to a low-temperature, low-pressure liquid by an expansion valve 5 and supplied to the evaporator 2. The above series of steps for cooling the air inside the vehicle is also called a refrigeration cycle.

[0015] The abnormality detection method according to the first embodiment of the present disclosure can be applied to the evaporator 2 or the condenser 4 (hereinafter, also collectively referred to as the heat exchanger 10).

[0016] Fig. 2A is a schematic diagram of a piping structure according to a first configuration example of a heat exchanger 10 according to a first embodiment of the present disclosure. Fig. 2B is a cross-sectional view of the piping structure of Fig. 2A. Fig. 2B is a cross-sectional view taken along line A-A' in Fig. 2A. The piping structure of Fig. 2A has a thermal insulation pipe 11 and tubes 12 arranged in the thermal insulation pipe 11. Air (heating medium) 13 flows between the thermal insulation pipe 11 and the tubes 12. A refrigerant 14 flows in the tubes 12.

[0017] In this specification, the direction in which the tubes 12 extend (i.e., the direction in which the refrigerant 14 flows) is referred to as a flow direction L. Furthermore, the radial direction of the tubes 12 is referred to as a radial direction R.

[0018] 2A shows an example in which the heat exchanger 10 is a parallel flow heat exchanger in which the air 13 and the refrigerant 14 flow in the same direction. However, the heat exchanger 10 is not limited to this, and may be a counter flow heat exchanger in which the air 13 and the refrigerant 14 flow in opposite directions, or a cross flow heat exchanger in which the air 13 and the refrigerant 14 flow perpendicular to each other.

[0019] The thermally insulated pipe 11 prevents heat exchange between the air 13 and refrigerant 14 inside the thermally insulated pipe 11 and the outside air outside the thermally insulated pipe 11. The tube 12 is, for example, a copper tube, and transfers heat between the air 13 and the refrigerant 14. The air 13 absorbs the heat of evaporation from the refrigerant 14 or releases the heat of condensation from the refrigerant 14 through the tube 12.

[0020] The refrigerant 14 is, for example, water. The refrigerant 14 may be in a gaseous state or a liquid state. The refrigerant 14 may also be a two-phase flow in which gas and liquid are mixed. In contrast to a two-phase flow, a refrigerant 14 in a single state of gas or liquid is also called a single-phase flow. The anomaly detection method according to the first embodiment of the present disclosure performs analysis assuming that the refrigerant 14 is a single-phase flow.

[0021] 3A and 3B are diagrams illustrating a first configuration example of a numerical analysis model according to the first embodiment of the present disclosure. The anomaly detection method according to the first embodiment of the present disclosure simplifies heat transfer, heat conduction, and advection in the heat exchanger 10 using a thermal network method.

[0022] Fig. 3A is a diagram showing heat exchanger 10 divided into multiple cells. The numerical analysis model of Fig. 3A has n unit structures 20, which are obtained by dividing the piping structure of Fig. 2A into n parts (n is any natural number) in the flow direction L. Each unit structure 20 can be divided into a cell for air 13 (hereinafter referred to as air cell 21), a cell for tube 12 (hereinafter referred to as metal cell 22), and a cell for refrigerant 14 (hereinafter referred to as refrigerant cell 23). Air cell 21, metal cell (piping cell) 22, and refrigerant cell 23 each have one calculation point 31, 32, and 33, respectively.

[0023] 3B is a diagram showing a thermal circuit 30. As shown in FIG. 3B, the heat transfer, heat conduction, and advection in the heat exchanger 10 can be simplified as a thermal circuit 30 having multiple calculation points 31, 32, and 33 as nodes.

[0024] Between two adjacent calculation points 31, there is heat transfer 34 due to the advection of the air 13. Between two adjacent calculation points 31 and 32, there is heat transfer 35 due to the heat transfer of the air 13 and the tubes 12. Between two adjacent calculation points 32, there is heat transfer 36 due to the thermal conduction of the tubes 12. Between two adjacent calculation points 32 and 33, there is heat transfer 37 due to the heat transfer of the refrigerant 14 and the tubes 12. Between two adjacent calculation points 33, there is heat transfer 38 due to the advection of the refrigerant 14.

[0025] In the numerical analysis model according to the first embodiment of the present disclosure, a governing equation based on the law of conservation of energy can be formulated for each of the plurality of calculation points 31 to 33.

[0026] 4A is a diagram illustrating the governing equations for the metal-air cell 21 according to the first embodiment of the present disclosure, illustrating the metal-air cells 21a and 21b, the metal cell 22a, and calculation points 31a, 31b, and 32a.

[0027] The air cell 21a is the cell to be analyzed and has a calculation point 31a. The air cell 21b is a cell adjacent to the air cell 21a in the opposite direction of the flow direction L, and supplies air 13 to the air cell 21a. The metal cell 22a is a cell adjacent to the air cell 21a, and exchanges heat with the air cell 21a. The air cell 21b and the metal cell 22a have calculation points 31b and 32a, respectively.

[0028] 4A is the i-th air cell 21 from the inlet boundary of the heat exchanger 10. The air cell 21b is the (i-1)th air cell 21. The metal cell 22a is the i-th metal cell 22.

[0029] Advection F from air cell 21b to air cell 21a ad_a,i is the flow rate of air 13 m a , specific heat c a , temperature T at calculation point 31b a,i-1 , temperature T at calculation point 31a a,i The heat transfer F between the metal cell 22a and the air cell 21a is expressed by the following formula (1).ht_am,i is the heat transfer coefficient a am , the surface area s of the contact surface of the air cell 21 and the metal cell 22 am , temperature T at calculation point 32a m,i When the heat exchanger 10 is in an equilibrium state, the advection F ad_a,i and heat transfer F ht_am,i The relationship is expressed by the following equation (3).

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[0030] By rearranging the above equations (1) to (3), the governing equation (4) below is derived.

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[0031] 4A does not exist, the advection F from the inlet boundary of the heat exchanger 10 to the air cell 21a. ad_a0 is the air temperature T at the inlet of the heat exchanger 10 a0 Using the above, it is expressed by the following equation (5). ad_ad0 is a boundary condition, and therefore, in this specification, the governing equation of the air cell 21 in contact with the inlet boundary is rearranged as shown in the following equation (6).

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[0032] 4B is a diagram illustrating the governing equations for the metal cell 22 according to the first embodiment of the present disclosure. FIG. 4B illustrates the air cell 21a, the metal cells 22a, 22b, and 22c, the refrigerant cell 23a, and the calculation points 31a, 32a, 32b, 32c, and 33a.

[0033] Metal cell 22a is the cell to be analyzed. Metal cell 22b is adjacent to metal cell 22a in the opposite direction of flow direction L and has calculation point 32b. Metal cell 22c is adjacent to metal cell 22a in flow direction L and has calculation point 32c. Air cell 21a is an air cell 21 adjacent to metal cell 22a. Refrigerant cell 23a is a refrigerant cell 23 adjacent to metal cell 22a and has calculation point 33a. Metal cells 22b and 22c, air cell 21a, and refrigerant cell 23a each exchange heat with metal cell 22a.

[0034] 4B is the i-th metal cell 22 from the inlet boundary of the heat exchanger 10. Metal cell 22b is the (i-1)th metal cell 22. Metal cell 22c is the (i+1)th metal cell 22. Air cell 21a is the i-th air cell 21. Refrigerant cell 23a is the i-th refrigerant cell 23.

[0035] Heat conduction F between the metal cells 22a and 22b tc,i-1 is the thermal conductivity k m , the surface area s of the cross section of the metal cell 22 m , the heat transfer distance (i.e., the distance between the calculation points 32 of two adjacent metal cells 22) ΔL, the temperature T of the calculation point 32b m,i-1 The thermal conduction F between the metal cells 22a and 22c is expressed by the following equation (7). tc,i is the temperature T at calculation point 32c m,i+1 The heat transfer F between the metal cell 22a and the refrigerant cell 23a is similarly expressed by the following equation (8). ht_mw,i is the heat transfer coefficient a mw , the surface area s of the contact surface of the metal cell 22 and the refrigerant cell 23 mw , temperature T at calculation point 33a w,i Using the above, the thermal conduction F is expressed by the following equation (9). tc,i-1 , F tc,i , heat transfer F ht_mw,i , and F ht_am,i The relationship is expressed by the following equation (10).

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[0036] By rearranging the above equations (7) to (10), the governing equation (11) below is derived.

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[0037] For the metal cell 22 (i.e., i=1) adjacent to the inlet boundary of the heat exchanger 10, there is no metal cell 22b in FIG. 4B. Therefore, the governing equation is the heat transfer F tc,i-1 is the given boundary condition b s Replace with (F tc,i-1 =b s Similarly, the governing equation for the metal cell 22 (i.e., i=n) that is in contact with the outlet boundary of the heat exchanger 10 is the heat conduction F tc,i is the given boundary condition b e Replace with (F tc,i =b e ) is expressed as the following equation (13). s , b e is, for example, 0.

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[0038] 4C is a diagram illustrating the governing equations for the refrigerant cell 23 according to the first embodiment of the present disclosure. FIG. 4C illustrates the metal cell 22a, the refrigerant cells 23a and 23b, and the calculation points 32a, 33a, and 33b.

[0039] Refrigerant cell 23a is the cell to be analyzed. Refrigerant cell 23b is a cell adjacent to refrigerant cell 23a in the opposite direction of flow direction L, and supplies refrigerant 14 to refrigerant cell 23a. Refrigerant cell 23b has calculation point 33b. Metal cell 22a is a cell adjacent to refrigerant cell 23a, and exchanges heat with refrigerant cell 23a.

[0040] 4C is the i-th refrigerant cell 23 from the inlet boundary of the heat exchanger 10. Refrigerant cell 23b is the (i-1)-th refrigerant cell 23. Metal cell 22a is the i-th metal cell 22.

[0041] Advection F from refrigerant cell 23b to refrigerant cell 23a ad_w,i is the flow rate m of the refrigerant 14 w , specific heat c w , temperature T at calculation point 33b w,i-1 Using the above, the advection F is expressed as follows: ad_w,i and heat transfer F ht_mw,i The relationship is expressed by the following equation (15).

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[0042] By rearranging the above equations (14) and (15), the governing equation (16) below is derived.

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[0043] 4C does not exist for the refrigerant cell 23 (i.e., i=1) that is in contact with the inlet boundary of the heat exchanger 10. The advection F from the inlet boundary of the heat exchanger 10 to the refrigerant cell 23a ad_w0 is the refrigerant temperature T at the inlet of the heat exchanger 10 w0 Using the above, it is expressed by the following equation (17): Furthermore, the governing equation of the refrigerant cell 23 in contact with the inlet boundary is expressed by the following equation (18).

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[0044] When the heat exchanger 10 is divided into n unit structures 20, the heat exchanger 10 has a total of 3n cells (i.e., n air cells 21, n metal cells 22, and n refrigerant cells 23). Therefore, a total of 3n governing equations (4), (6), (11), (12), (13), (16), and (18) are formulated. The 3n governing equations can be expressed as a determinant, as in the following equation (19).

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[0045] The variable matrix T is the temperature T of calculation points 31 to 33. a,1 ~T a,n , T m,1 ~T m,n , and T w,1 ~T w,n The boundary condition matrix b is a matrix that stores the boundary conditions of the heat exchanger 10, and the matrix size is 1×3n. Specifically, the boundary condition matrix b stores the right-hand sides of the governing equations (4) to (18). The coefficient matrix A is a matrix that stores the temperature T a,1 ~T a,n , T m,1 ~T m,n , and T w,1 ~T w,n is a matrix that stores the coefficients of each term in the matrix, and the matrix size is 3n × 3n.

[0046] 5 is a flowchart showing a procedure for outputting a temperature distribution according to the first embodiment of the present disclosure. The flowchart in FIG. 5 is calculated, for example, by an abnormality detection device described below.

[0047] First, physical property values, values ​​calculated from the shape of the heat exchanger 10, and values ​​measured by a predetermined sensor are acquired (step S1). a From the shape of the heat exchanger 10, for example, the surface area s of the contact surface between the air cell 21 and the metal cell 22 is obtained. am The measured value by the sensor is, for example, the air temperature T a0 is obtained.

[0048] Next, based on the values ​​acquired in step S1, the coefficient matrix A and boundary condition matrix b in equation (19) are created (step S2).

[0049] Furthermore, equation (19) is solved for the variable matrix T (step S3). In step S3, equation (19) can be solved by a computer using, for example, the LU (Lower-Upper) decomposition method, the SOR (Successive Over-Relaxation) method, the conjugate gradient method, the Krylov subspace method, or the like.

[0050] The temperatures of the calculation points 31 to 33 can be extracted from the elements of the variable matrix T, that is, the temperature distribution of the heat exchanger 10 can be determined (step S4).

[0051] As a comparative example, the temperature distribution of the heat exchanger 10 can also be calculated by the differential equations shown in the following formulas (20) to (22). a , T m , and T w are the temperatures of the air 13, the tubes 12, and the refrigerant 14, respectively.

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[0052] The governing equations (4) to (18) according to the first embodiment of the present disclosure are obtained by discretizing the differential equations (20) to (22). The governing equations (4) to (18) are simpler calculation formulas than the differential equations (20) to (22), and are therefore more suitable for deriving the temperature distribution using a computer.

[0053] FIG. 6A is a diagram showing the temperature distribution of the heat exchanger 10 according to the first configuration example, derived using the temperature distribution output procedure according to the first embodiment of the present disclosure. FIG. 6B is a diagram showing the temperature distribution of the heat exchanger 10 according to the first configuration example, derived using a differential equation according to a comparative example. FIGS. 6A and 6B illustrate, from above, the temperature distribution of the air 13, the temperature distribution of the tubes 12, and the temperature distribution of the refrigerant 14. The horizontal axes of FIGS. 6A and 6B represent the unit structures 20 divided in the flow direction L of the heat exchanger 10. The shading of the temperature distribution of the air 13, the tubes 12, and the refrigerant 14 indicates the temperatures of the unit structures 20, with darker areas indicating higher temperatures.

[0054] 6A and 6B, the temperature distribution of the heat exchanger according to the first configuration example output by the procedure according to the present disclosure is substantially identical to the temperature distribution derived by the procedure according to the comparative example. Therefore, the temperature distribution output procedure according to the present disclosure can simplify the calculations compared to the comparative example without affecting the calculation accuracy.

[0055] The inventors used the following conditions to derive the temperature distributions in Figures 6A and 6B. Table 1 is a table summarizing the physical properties of the air 13, the tubes 12, and the refrigerant 14 at 0°C. Table 2 is a table summarizing information on the external shape of the heat exchanger 10. Table 3 is a table summarizing boundary conditions. In addition, in this verification, superLU was used to calculate equation (19). [Table 1] [Table 2] [Table 3]

[0056] The anomaly detection method according to the first embodiment of the present disclosure may be applied to a fin-and-tube heat exchanger. Fig. 7A is a schematic diagram of a piping structure according to a second configuration example of the heat exchanger 10 according to the first embodiment of the present disclosure. The heat exchanger 10a shown in Fig. 7A is a fin-and-tube heat exchanger having a structure in which a plurality of fins 42 are attached to a tube 41 through which a refrigerant 14 flows. The plurality of fins 42 are arranged in the flow direction L of the refrigerant 14. Furthermore, air 13 flows between two adjacent fins 42 in a direction U intersecting the flow direction L of the refrigerant 14.

[0057] The fins 42 are used to increase the heat transfer area of ​​the heat exchanger 10a and are made of, for example, metal. The fins 42 in Fig. 7A have, for example, a plate shape, but are not limited to this and the shape of the fins 42 is arbitrary.

[0058] Figure 7B is a one-dimensional representation of the heat exchanger 10a of Figure 7A. As shown in Figure 7B, the heat exchanger 10a has a plurality of repeating structures 43, each having a tube 41 and a fin 42. By discretizing the plurality of repeating structures 43, a numerical analysis model of the heat exchanger 10a can be formed.

[0059] 7C is a diagram showing a second configuration example of a numerical analysis model according to the first embodiment of the present disclosure. The numerical analysis model in FIG. 7C has n unit structures 20a obtained by dividing the piping structure in FIG. 7A into n parts (n is any natural number) in the flow direction L. Each unit structure 20a can be divided into an air cell 21, a cell of fins 42 (hereinafter referred to as a fin cell 51), a cell of tubes 41 (hereinafter referred to as a tube cell 52), and a refrigerant cell 23. The fin cell 51 and the tube cell 52 each have one calculation point 61 and one calculation point 62.

[0060] The fin cell 51 may include a plurality of fins 42 arranged continuously in the flow direction L. Below, an example will be described in which the fin cell 51 is composed of a plurality of fins 42 (hereinafter also referred to as a fin group).

[0061] 8A is a diagram illustrating the governing equations for the fin cell 51 according to the first embodiment of the present disclosure. 8A illustrates the air cell 21a, the fin cells 51a, 51b, and 51c, the tube cell 52a, and the calculation points 31a, 61a, 61b, 61c, and 62a.

[0062] The fin cell 51a is the cell to be analyzed and has a calculation point 61a. The fin cell 51b is adjacent to the fin cell 51a in the opposite direction of the flow direction L and has a calculation point 61b. The fin cell 51c is adjacent to the fin cell 51a in the flow direction L and has a calculation point 61c. The air cell 21a is the air cell 21 adjacent to the fin cell 51a. The tube cell 52a is the tube cell 52 adjacent to the fin cell 51a and has a calculation point 62a.

[0063] 8A is the i-th fin cell 51 from the inlet boundary of the heat exchanger 10. Fin cell 51b is the (i-1)th fin cell 51. Fin cell 51c is the (i+1)th fin cell 51. Air cell 21a is the i-th air cell 21. Tube cell 52a is the i-th tube cell 52.

[0064] Between two adjacent calculation points 61, there is heat transfer 53 due to the thermal conduction of the fin group. Between two adjacent calculation points 31 and 61, there is heat transfer 35 due to the heat transfer between the air 13 and the fin group. Between two adjacent calculation points 61 and 62, there is heat transfer 54 due to the heat transfer between the fin group and the tube 41.

[0065] The heat transfer 54 between the fin cell 51 and the tube cell 52 occurs at an intermediate temperature T s The heat conduction on the fin side, F ht_f and the thermal conduction F of the tube side ht_t The equilibrium condition for and is the thermal conductivity of the fin group k f , the thermal conductivity k of the tube 41 t , the distance Δr between the calculation points 61 and 62, the surface area s of the contact surface of the fin cell 51 and the tube cell 52 ft , the temperature T of the calculation point 61 (i.e., the fin group) f , and the temperature T of the calculation point 62 (i.e., the tube 41) t Using the above, it is expressed by the following equations (23) to (25).

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[0066] Furthermore, equation (25) can be rearranged into the following equations (26) and (27).

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[0067] The governing equation for the fin cell 51a is the heat transfer coefficient a between the air 13 and the fin group. af , the surface area s of the contact surface of the air cell 21 and the fin cell 51af , the surface area s of the contact surface of the two fin cells 51 f , the heat transfer distance ΔL between the two fin cells 51 f , the temperature T of the calculation points 61a, 61b, 61c, and 62a f,i , T f,i-1 , T f,i+1 , T t,i , and the heat transfer F in Eq. (27) ht_f,i Using this, it is expressed by the following equation (28).

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[0068] 8B is a diagram illustrating the governing equations for the tube cell 52 according to the first embodiment of the present disclosure. Fig. 8B illustrates the fin cell 51a, the tube cells 52a, 52b, and 52c, the refrigerant cell 23a, and the calculation points 61a, 62a, 62b, 62c, and 33a.

[0069] Tube cell 52a is the cell to be analyzed. Tube cell 52b is adjacent to tube cell 52a in the opposite direction of flow direction L and has calculation point 62b. Tube cell 52c is adjacent to tube cell 52a in flow direction L and has calculation point 62c. Refrigerant cell 23a is a refrigerant cell 23 adjacent to tube cell 52a.

[0070] 8B is the i-th tube cell 52 from the inlet boundary of the heat exchanger 10. Tube cell 52b is the (i-1)th tube cell 52. Tube cell 52c is the (i+1)th tube cell 52. Refrigerant cell 23a is the i-th refrigerant cell 23.

[0071] The governing equation for the tube cell 52a is the heat transfer coefficient a between the refrigerant 14 and the tube 41. tw , the surface area s of the contact surface of the tube cell 52 and the refrigerant cell 23 tw , the surface area s of the contact surface of the two tube cells 52 t , the heat transfer distance ΔL between the two tube cells 52 t , temperature T at calculation points 62b and 62cf,i-1 , T f,i+1 , and the heat transfer F in Eq. (27) ht_t,i Using this, it is expressed by the following equation (29).

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[0072] The governing equations for the air cells 21a and the refrigerant cells 23a of the heat exchanger 10a are expressed by the following equations (30) and (31).

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[0073] A total of 4n governing equations (28) to (31) can be formulated, which can be expressed as a determinant as in equation (19). The matrix sizes of the coefficient matrix A, variable matrix T, and boundary condition matrix b are 4n × 4n, 1 × 4n, and 1 × 4n, respectively.

[0074] As a comparative example, the temperature distribution of the fin-and-tube heat exchanger 10a can also be analyzed by creating a CAE (Computer Aided Engineering) model. Fig. 9A is a diagram showing the temperature distribution of the heat exchanger 10a according to a second configuration example, derived using the temperature distribution output procedure according to the first embodiment of the present disclosure. Fig. 9B is a diagram showing the temperature distribution of the heat exchanger 10a according to the second configuration example, derived using a CAE model according to a comparative example. Figs. 9A and 9B illustrate, from above, the temperature distribution of the air 13, the temperature distribution of the fins, the temperature distribution of the tubes 41, and the temperature distribution of the refrigerant 14.

[0075] 9A and 9B, the temperature distribution of the heat exchanger according to the second configuration example output by the procedure according to the present disclosure is substantially identical to the temperature distribution derived by the CAE model according to the comparative example. The temperature distribution analysis according to the procedure according to the present disclosure requires less calculation effort than a temperature distribution analysis using a CAE model. The temperature distribution output procedure according to the present disclosure can reduce the calculation load without affecting the calculation accuracy compared to the comparative example.

[0076] The inventors used the following conditions to derive the temperature distributions in Figures 9A and 9B. Table 4 is a table summarizing information on the outer shape of the heat exchanger 10a. Table 5 is a table summarizing boundary conditions. Note that values ​​not listed below use the same values ​​as in Tables 1 to 3. Furthermore, the tubes 41 are made of, for example, copper, and the fins 42 are made of, for example, aluminum. Furthermore, in this verification, a CAE model according to a comparative example is created using 1D-CAE. [Table 4] [Table 5]

[0077] As described above, the temperature distribution of the heat exchanger 10 or 10a (hereinafter also simply referred to as the heat exchanger 10) can be analyzed by solving equation (19). Here, in order to solve equation (19), the physical quantities included in matrices A and b must be given as known quantities. The physical quantities in matrices A and b are input as physical property values ​​(e.g., specific heat, etc.), values ​​calculated from the shape of the heat exchanger 10 (e.g., the surface area of ​​the contact surfaces between cells, etc.), or measurements taken by a sensor (e.g., the temperature at the inlet boundary of the heat exchanger 10, etc.).

[0078] However, among the elements of matrices A and b, there are elements that are difficult to measure using sensors. For example, the flow rates m of the air 13 and the refrigerant 14 a and m w Since it is difficult to measure with a sensor, an empirically obtained value is generally input. Depending on the type of heat exchanger 10, this empirically obtained value may not be appropriate, and in that case, the accuracy of the temperature distribution calculation by equation (19) will decrease.

[0079] Furthermore, the conventional thermal network method can only analyze the temperature distribution of the heat exchanger 10. Therefore, the thermal network method cannot directly detect abnormalities related to physical quantities other than temperature. For example, the thermal network method cannot directly detect abnormalities such as clogging of the tube 12 or 41 (hereinafter simply referred to as the tube 12).

[0080] The anomaly detection method according to the first embodiment of the present disclosure is characterized by being able to solve these problems.

[0081] Table 6 below summarizes the physical quantities used in the thermal network method, whether they can be obtained by sensors, and whether they are used as known quantities in the thermal network method. [Table 6]

[0082] As shown in Table 6, the flow rates m of the air 13 and the refrigerant 14 a and m w is used as a known quantity in the thermal network method, but it is difficult to measure it using a sensor in practice. On the other hand, the temperature T of the air 13 at the outlet of the heat exchanger 10 a,n and the temperature T of the refrigerant 14 w,n is generally solved as an unknown in the thermal network method. However, the temperature T a,n and T w,n can be easily measured by providing a sensor at the outlet of the heat exchanger 10 without solving it using the thermal network method.

[0083] In addition, the pressure of air 13 p a is the specific heat c a is a parameter that determines the pressure p of the refrigerant 14. w is the specific heat c w is a parameter that determines

[0084] In the anomaly detection method according to the present disclosure, a known physical quantity (i.e., temperature T a,n and T w,n ) into the unknown physical quantity (i.e., the flow rate m a and m w), the thermal network method can be applied to the unknown physical quantities (flow rate m a and m w ) is treated as an inverse problem to find the temperature T a,n , T w,n , flow rate m a , and m w Furthermore, the anomaly detection method according to the present disclosure may treat three or more physical quantities as known physical quantities by adding sensors, etc. In this case, the same number of physical quantities as the number of known physical quantities (or a number less than that) that are difficult to measure with sensors can be solved as unknown physical quantities.

[0085] In this specification, data that can be acquired by the sensor, specifically the temperature T a,n and T w,n and the temperature at the inlet boundary T a0 and T w0 are also called known data. Also, the temperature T a,n and T w,n Temperature other than T a,1 ~T a,n-1 , T m,1 ~T m,n , T w,1 ~T w,n-1 is called the first unknown data. In addition, the element in the coefficient matrix A to be exchanged with the known data, specifically the flow rate m a and m w is called the second unknown data.

[0086] Equation (19) is expressed as the temperature T in the variable matrix T. a,n , T w,n Each element except for and the flow rate m a and m w When equations for and are defined, equation (19) becomes a nonlinear equation. The anomaly detection method according to the present disclosure solves equation (19) as a nonlinear optimization problem.

[0087] The anomaly detection method according to the present disclosure solves equation (19) using any one of nonlinear equation solving methods such as the Newton-Raphson method, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, the Nelder-Mead method, the Powell method, or a brute force method, or a data assimilation method such as a four-dimensional variational method or a Kalman filter. Below, a method for solving equation (19) using a brute force method will be described as an example.

[0088] The inventors have used sensors to measure the temperature T of the air 13 and the refrigerant 14 at the inlet boundary of the heat exchanger 10. a0 =40.0, T w0 = 20.0, and the temperature T of the air 13 and the refrigerant 14 at the outlet boundary of the heat exchanger 10 a,n =32.0, T w,n = 26.0 and the pressure of air 13 p a = 1.013 and the pressure of refrigerant 14, p w =1.013 was obtained.

[0089] In the brute force search method, the flow rate m a and m w Substitute a temporary value into and make it a known value. Also, the temperature T a,n and T w,n As unknowns, equation (19) is solved for the variable matrix T. The temperature T obtained by solving equation (19) is a,n and T w,n (hereinafter also referred to as the optimized value) is compared with the value acquired by the sensor (hereinafter also referred to as the sensor value). To solve equation (19), LU decomposition or the like can be used, as in step S3 of FIG.

[0090] In the brute force search method, the flow rate m a and m w By solving equation (19) multiple times while changing the temporary value substituted into , the temperature T a,n and T w,n The optimized value of m and the sensor value are almost the same. a and m w In this disclosure, the temperature T a,n and T w,nA global optimization is performed to minimize the mean square error between the optimized value of and the sensor value. a and m w The search range of each is 0.00≦m a ≦5.0×10 -2 , 0.00≦m w ≦5.0×10 -2 Let the flow rate m a and m w A value within this search range is substituted as a temporary value for .

[0091] The inventors have used the above method to measure the temperature T a,n and T w,n The optimized value and the obtained value of flow rate m a and m w As the value of m a =1.65×10 -2 , and m w =5.00×10 -3 10 is a waveform diagram showing the relationship between the number of calculations and the error by the brute force search method according to the first embodiment of the present disclosure. The horizontal axis of FIG. 10 shows the number of times the equation (19) is solved, and the vertical axis shows the temperature T a,n and T w,n 10 shows the mean square error between the optimized value and the sensor value. As shown in FIG. 10, in the brute force search method of the present disclosure, the error converges after about 150 calculations.

[0092] In the above method, values ​​that are difficult to measure using a sensor are no longer treated as known quantities, and therefore the calculation accuracy of the thermal network method can be improved. In addition, since the thermal network method can analyze physical quantities other than temperature, it becomes possible to directly detect abnormalities other than temperature. For example, the flow rate m a and m w Solve and find the flow rate m a and m w By comparing this with a predetermined threshold, it becomes possible to detect abnormalities in the flow rate due to clogging of the tube 12 or the like.

[0093] 11 is a block diagram showing an anomaly detection device 70 to which the anomaly detection method according to the first embodiment of the present disclosure is applied. The anomaly detection device 70 includes an input data acquisition unit (input unit) 71, a model generation unit 72, a calculation unit 73, a determination unit 74, and a result output unit (output unit) 75.

[0094] The input data acquisition unit 71 acquires physical property values ​​and shape information of the heat exchanger 10 input via a GUI (Graphical User Interface) or the like. The input data acquisition unit 71 also acquires measured values ​​from sensors. The model generation unit 72 generates a numerical analysis model of the heat exchanger 10 by a thermal network method and formulates Equation (19). The calculation unit 73 performs calculations including solving Equation (19) to calculate the temperature distribution and the flow rate m a and m w The determination unit 74 determines whether an abnormality has occurred in the heat exchanger 10 based on whether the value calculated by the calculation unit 73 exceeds a predetermined threshold value. The result output unit 75 outputs the determination result of the determination unit 74 to a monitor or the like.

[0095] The abnormality detection device 70 may be configured to include a storage unit 76. The storage unit 76 may store physical property values, shape information of the heat exchanger 10, and the like input from the input data acquisition unit 71, etc. Furthermore, the calculation unit 73 may perform calculations based on the physical property values, etc. stored in the storage unit 76, and the determination unit 74 may perform the above-mentioned determination based on threshold values, etc. stored in the storage unit 76.

[0096] As described above, the anomaly detection method according to the first embodiment of the present disclosure forms a numerical analysis model in which a parallel flow heat exchanger and a fin-and-tube heat exchanger are divided into a plurality of unit structures 20 (and 20a). Furthermore, the anomaly detection method according to the present disclosure formulates linear equations based on the law of conservation of energy for the air cells 21, metal cells 22 (or fin cells 51 and tube cells 52), and refrigerant cells 23 for each unit structure 20 (and 20a). The calculation unit 73 can calculate the temperature distribution of the heat exchanger 10 by combining these plurality of linear equations into a determinant and performing calculations.

[0097] The anomaly detection method according to the present disclosure has the same level of calculation accuracy as analysis using differential equations and analysis using CAE models. Furthermore, the anomaly detection method according to the present disclosure can simplify calculations compared to analysis methods using differential equations, making it easier for computers to perform calculations. Furthermore, the anomaly detection method according to the present disclosure can reduce the calculation load on a computer compared to analysis using CAE models.

[0098] Furthermore, the abnormality detection method according to the present disclosure can be applied to parameters that are treated as known quantities in the thermal network method but are difficult to measure using sensors (for example, the flow rates m a and m w ) and parameters that are treated as unknowns but are easy to measure with sensors (for example, the outlet temperatures T a,n and T w,n ) and replace the above determinant with the flow rate m a and m w The anomaly detection method according to the present disclosure solves the problem of calculating the temperature as an inverse problem. This eliminates the need to treat values ​​that are difficult to measure using sensors as known quantities, thereby improving the calculation accuracy of the thermal network method. Furthermore, since the thermal network method can analyze physical quantities other than temperature, it becomes possible to detect anomalies other than temperature (for example, clogging of the tube 12).

[0099] In other words, the anomaly detection technique according to the present disclosure can theoretically calculate physical quantities that are difficult to read with sensors.

[0100] (Second embodiment) FIG. 12 is a diagram showing the state transition of the refrigerant 14 in the heat exchanger 10 according to the second embodiment of the present disclosure. The refrigerant 14 in the heat exchanger 10 can be in either a liquid or gas state. In the tubes 12, there are cases where liquid refrigerant 14a flows, cases where gas refrigerant 14b flows, and cases where a gas-liquid mixed refrigerant 14c, which is a mixture of the liquid refrigerant 14a and the gas refrigerant 14b, flows. In this specification, the case where either the liquid refrigerant 14a or the gas refrigerant 14b flows in the tubes 12 is referred to as single-phase flow. In addition, the case where the gas-liquid mixed refrigerant 14c flows in the tubes 12 is referred to as two-phase flow.

[0101] The first embodiment of the present disclosure is based on the premise that the heat exchanger 10 is a single-phase flow type. In contrast, the abnormality detection method according to the second embodiment of the present disclosure can also be applied to a two-phase flow type heat exchanger 10.

[0102] There are two issues in detecting abnormalities in two-phase flow. The first issue is that in two-phase flow, physical properties can change significantly depending on the temperature. For example, the specific heat c of the refrigerant 14 w changes with temperature, so the specific heat c w When the calculation is performed with a fixed value, the error in the inflow enthalpy of the refrigerant 14 becomes large. w (T w ), the governing equations such as equation (16) become nonlinear with respect to temperature, resulting in more complex calculations and an increased amount of calculations.

[0103] The second challenge is that the latent heat component of the enthalpy change due to advection of the refrigerant 14 (hereinafter also referred to as inflow enthalpy) must be taken into consideration. The inflow enthalpy of the refrigerant 14 includes a sensible heat component used to change the refrigerant temperature and a latent heat component used to change the state of the refrigerant. The governing equation of the refrigerant 14 (Equation (16), etc.) in the first embodiment can analyze the sensible heat component, which is linear with respect to temperature. In the second embodiment, in addition to the sensible heat component, it is necessary to analyze the latent heat component, which is nonlinear with respect to temperature. This makes the governing equation nonlinear, which complicates computer calculations and increases the amount of calculations.

[0104] The anomaly detection method according to the second embodiment of the present disclosure is characterized by being able to solve the above two problems.

[0105] 13 is a flowchart showing a procedure for outputting a temperature distribution according to the second embodiment of the present disclosure. The procedure for outputting a temperature distribution according to the second embodiment of the present disclosure is characterized in that the temperature distribution is derived by iterative calculation while sequentially updating physical property values.

[0106] First, temperature-independent physical property values, the shape of the heat exchanger 10, and measurements by a predetermined sensor are acquired (step S1). Next, temperature-dependent physical property values ​​(hereinafter simply referred to as physical property values) are updated (step S11). The physical property values ​​may be updated with appropriate initial values ​​the first time. Alternatively, an appropriate temperature distribution may be set as the initial temperature distribution, and the physical property values ​​may be set based on the initial temperature distribution.

[0107] Based on the physical property value φ updated in step S11, the coefficient matrix A is updated (step S12). Next, based on the updated coefficient matrix A, equation (19) is solved for the variable matrix T. To solve equation (19), the LU decomposition method, the SOR method, or the like can be used, as in step S3 of FIG. 5. Furthermore, based on the temperature value of each cell in the variable matrix T, the temperature distribution in the heat exchanger 10 is updated (step S13).

[0108] 13, steps S11 to S13 are repeated until the temperature distribution updated in step S13 converges. Based on the temperature of each cell calculated in step S13, the physical property value φ of each cell is updated in step S11. Subsequently, the coefficient matrix A is updated in step S12, and the temperature distribution is updated in step S13.

[0109] After the temperature distribution is updated in step S13, it is determined whether the calculation has converged (step S14). In step S14, for example, the temperature distribution before the update is compared with the temperature distribution after the update. If the difference in temperature between each cell is within a predetermined threshold, it can be determined that the calculation has converged. If the calculation has not converged, the solution of equation (19) is performed again in steps S11 to S13. If the calculation has converged, the temperature distribution of the heat exchanger 10 is output from the result output unit 75 or the like.

[0110] The updating of the physical property values ​​in steps S11 and S12 will be described in more detail below. For example, in a two-phase flow, the advection F′ between the refrigerant cells 23a and 23b in FIG. 4C ad_w,i is the specific heat (constant pressure specific heat) function c w (Tw ) is expressed by the following equation (32).

number

[0111] In addition, h in equation (32) i and h i-1 are the enthalpies of the refrigerant 14 in the refrigerant cells 23a and 23b, respectively. Δh is the enthalpy difference between the refrigerant cells 23a and 23b.

[0112] As shown in equation (32), the advection F' in the case of two-phase flow ad_w,i is the temperature T w Since the equation is nonlinear with respect to , the computation becomes complicated.

[0113] 14 is a waveform diagram showing the relationship between the temperature and specific heat of the refrigerant cells 23a and 23b according to the second embodiment of the present disclosure. The horizontal axis of FIG. 14 represents the temperature, and the vertical axis represents the specific heat. FIG. 14 shows the relationship between the temperature T w,i From the above, the temperature T of the refrigerant cell 23b w,i-1 The curve W1 is a graph showing the temperature T w,i From T w,i-1 Specific heat up to c w (T w ) and the area of ​​the region enclosed by the curve W1 and the horizontal axis of FIG. 14 corresponds to the enthalpy difference Δh between the refrigerant cells 23a and 23b.

[0114] The area Δh of the region in FIG. 14 can be approximated by the trapezoidal rule. Specifically, if the region in FIG. 14 is N T When divided into pieces, Δh can be approximated by the following equations (33) and (34).

number

[0115] Using equations (32) and (33), the advection F' ad_w,i is rearranged as shown in the following equation (35).

number

[0116] As shown in equation (35), the advection F' ad_w,i is the coefficient m w c p / 2N T Temperature T w,i and T w,i-1 It can be expressed as a linear equation. ad_w,i can be linearized using the trapezoidal rule or a numerical integration method (e.g., Gauss-Legendre quadrature).

[0117] Coefficient m w c p / 2N T can be stored as one element of the coefficient matrix A. In steps S12 and S13, the coefficient m w c p / 2N T Update.

[0118] Coefficient m w c p / 2N T Among them, coefficient c p is the temperature T w,i From T w,i-1 Each temperature T w Specific heat c according to w (T w ) is expressed as the sum of the temperature T w Specific heat c w (T w ) to obtain the coefficient m w c p / 2N T can be updated.

[0119] Specific heat c w (T w ) may be calculated from a predetermined relational expression, etc. w Specific heat c per unit temperature and pressure p w (T w ) may be stored in advance in the storage unit 76. In this case, in steps S12 and S13, the temperature T w , and the pressure p applied to the refrigerant 14, the specific heat cw (T w ) can be extracted.

[0120] Specific heat c stored in advance in the memory unit 76 w (T w ) may be a value obtained experimentally or a value calculated by CAE or the like.

[0121] In addition, the specific heat c w (T w ) is stored, the specific heat c extracted from the memory unit 76 w (T w ) data may be interpolated. Details of the interpolation will be described later.

[0122] As described above, for each cycle of the iterative calculation (i.e., steps S11 to S13), w Specific heat c w (T w ) and obtain the coefficient m w c p / 2N T By inputting the coefficient m w c p / 2N T can be made a constant. This allows us to obtain the advection F' as shown in equation (35). ad_w,i at temperature T w This can be linearized in terms of , simplifying the computational solution.

[0123] The memory unit 76 stores the specific heat c w (T w ) and various physical property values ​​used to update the coefficient matrix A can be stored. For example, the temperature T w , and pressure p are two intensive variables that determine the state of the physical property φ(T w , p), the temperature T w , pressure p, and physical property φ.

[0124] 15A to 15C are diagrams showing an example of data sets stored in the storage unit 76 according to the second embodiment of the present disclosure. FIG. 15A shows a data set for the quality fraction x. Two axes in the planar direction of FIG. 15A represent the pressure p and the temperature T of the refrigerant 14, respectively. w The vertical axis of FIG. 15A represents the dryness fraction x. Each point plotted in FIG. 15A represents one data set (T w , p, φ). In FIG. 15A, the physical property value φ is the dryness fraction x.

[0125] Figure 15B shows the density σ [kg / m 3 ]. The height axis of FIG. 15B shows the density σ. FIG. 15C shows the data set for specific enthalpy h [J / kg]. The height axis of FIG. 15C shows the specific enthalpy h.

[0126] As described above, the storage unit 76 stores the temperature T w The physical property values ​​φ for each temperature T may be stored discontinuously. In this case, a data set corresponding to the temperature of the heat exchanger 10 may not be stored in the storage unit 76. w Even among multiple data sets with similar values, the physical property value φ can vary greatly. For this reason, if the physical property value φ in a data set is applied directly to the coefficient matrix A, the physical property value will fluctuate significantly with each iteration, which may lead to divergence in the analysis results.

[0127] Therefore, the anomaly detection method according to the second embodiment of the present disclosure solves the above problem by interpolating the data set acquired from the storage unit 76. FIGS. 16A and 16B are diagrams showing the interpolation method for the physical property value φ. First, before step S11 in FIG. 13, the temperature T w and pressure p are obtained. Hereinafter, the calculation point for obtaining the physical property value φ is referred to as the calculation point P0(p,T w ) is called

[0128] Next, the temperature T of the calculation point is read from the memory unit 76. wThree data sets of temperature and pressure near the temperature and pressure p are acquired. w1 ), P2(p2,T w2 ), and P3(p3,T w3 ) is called

[0129] 16A, the area of ​​the triangle formed by the calculation point P0 and the data sets P1 and P2 is defined as Δ1. The area of ​​the triangle formed by the calculation point P0 and the data sets P1 and P3 is defined as Δ2. The area of ​​the triangle formed by the calculation point P0 and the data sets P1 and P2 is defined as Δ3.

[0130] 16B, the physical property value φ of the calculation point P0 can be calculated from the relationship between the physical property values ​​φ1, φ2, and φ3 of the data sets P1, P2, and P3 and the above areas Δ1, Δ2, and Δ3. Specifically, it is as shown in the following equation (36).

number

[0131] In addition, λ in Equation (36) j are area coordinates, e.g., λ1=Δ1 / (Δ1+Δ2+Δ3).

[0132] 16A and 16B illustrate a method of area interpolation according to a second embodiment of the present disclosure using three data sets. Note that the anomaly detection method of the present disclosure may also use area interpolation using four or more data sets. Alternatively, the physical property value φ may be interpolated from the temperature ratio of two data sets, etc.

[0133] The variables (pressure p, etc.) required to uniquely determine the physical property values ​​in step S11 may be set together when setting the initial temperature distribution. Also, the physical property values ​​acquired in step S11 may be used to calculate other physical property values ​​in the same step S11. Specifically, the temperature T w The dryness fraction x (and density σ), whose state quantity is determined by the dryness fraction x (and density σ), may be used to calculate the two-phase flow heat transfer coefficient a, whose state quantity is determined by the dryness fraction x (and density σ).

[0134] The physical property values ​​stored in the storage unit 76 are w and pressure p, but may be determined by three or more parameters, or by temperature T w The physical property value may be uniquely determined by either the temperature T w The temperature-dependent physical property value may be uniquely determined by a parameter other than the pressure p. The temperature-dependent physical property value may be updated not only for the refrigerant 14 but also for the tube 12 or the air 13 in step S11.

[0135] 13, a method for updating physical property values ​​using the temperature distribution, etc., from one cycle before the iterative calculation has been described. However, in step S11, a high-order explicit method (for example, the Adams-Bashfors method) may be used, which updates physical property values ​​using the temperature distribution from two or three cycles before the iterative calculation.

[0136] The iterative calculation described in FIG. 13 can solve the first problem in two-phase flow. That is, even when the physical property value φ of the refrigerant 14 or the like changes with temperature, the temperature distribution can be calculated with high accuracy with a relatively small amount of calculation. The method disclosed herein can reduce errors in inflow enthalpy and the like compared to calculations using a fixed temperature-dependent physical property value φ. Furthermore, the method disclosed herein can solve a linearized version of Equation (19) at each step of the iterative calculation. Therefore, the calculation can be simplified and the amount of calculation can be reduced compared to treating the physical property value φ as a temperature function φ(T) and making the determinant of Equation (19) nonlinear.

[0137] The iterative calculation in FIG. 13 is not limited to two-phase flow, but can also be applied to single-phase flow.

[0138] Next, a method for solving the second problem will be described. Fig. 17 is a diagram showing the enthalpy amount h across the two-phase region according to the second embodiment of the present disclosure. a From the liquid refrigerant 14a having a predetermined enthalpy h bThe amount of inflow enthalpy Δh required to change the refrigerant into gas refrigerant 14b having the following structure: a-b is expressed by the following equations (37) to (40).

number

[0139] Δh a-SL is the enthalpy h a The enthalpy h of the liquid refrigerant 14a changing into the gas-liquid mixed refrigerant 14c is SL is the enthalpy (i.e., the sensible heat component) required to raise the temperature to SL-SG is the enthalpy (i.e., the latent heat component) for changing the state of the liquid refrigerant 14a to the gas refrigerant 14b. SG-b is the enthalpy h immediately after the liquid refrigerant 14a becomes the gas refrigerant 14b. SG from enthalpy h b is the enthalpy (i.e., of the sensible heat component) to raise the temperature to

[0140] x SL is the quality just before the liquid refrigerant 14a becomes the gas-liquid mixed refrigerant 14c, for example, x SL = 0. x SG is the quality of the gas-liquid mixture refrigerant 14c immediately after it becomes the gas refrigerant 14b, for example, x SG =0.

[0141] As shown in equation (39), the enthalpy of the latent heat component Δh SL-SG can be expressed linearly with respect to the dryness fraction x using a predetermined coefficient r.

[0142] Furthermore, the sensible heat components of equations (38) and (40) can be approximated to linear equations using the methods shown in FIGS.

[0143] The refrigerant 14 in a single phase state (i.e., the liquid refrigerant 14a and the gas refrigerant 14b) changes temperature T w changes, but the dryness x is x SL or x SGTherefore, the governing equation for the refrigerant cell 23 of the liquid refrigerant 14a or the gas refrigerant 14b is given by the temperature T w can be approximated by a linear equation for

[0144] The two-phase refrigerant 14 (i.e., the gas-liquid mixture refrigerant 14c) has a dryness of x due to an increase or decrease in the inflow enthalpy. SL ~x SG The temperature T w is constant. Therefore, the governing equation for the refrigerant cell 23 of the gas-liquid mixture refrigerant 14c is w This is a linear equation for the dryness fraction x, with

[0145] Therefore, in the anomaly detection method according to the second embodiment of the present disclosure, the variable matrix T in equation (19) is expanded so that the dryness fraction x can be stored. That is, the variable matrix T according to the second embodiment of the present disclosure is expanded so that the temperature T a,1 ~T a,n , T m,1 ~T m,n , and T w,1 ~T w,n And the dryness fraction x1~x at calculation point 33 n It is a 1x4n matrix that stores the

[0146] In the heat exchanger 10, 3n governing equations can be formulated for each cell. The 4n variables in the variable matrix T are the temperature T w Since either the dryness fraction x or the dryness fraction x is a known quantity, the number of unknowns is 3n. In other words, the number of unknowns matches the governing equation, so the unknowns can be uniquely identified.

[0147] In order to make the coefficient matrix A a square matrix, a dummy governing equation may be added. Alternatively, the variable matrix T may be a 1×3n matrix containing only unknowns. In this case, in the governing equation of the refrigerant cell 23, the temperature T w Alternatively, one of the terms of the dryness fraction x, which is a known quantity, may be treated as a boundary condition.

[0148] The method for solving the second problem also involves iterative calculations as shown in Fig. 13. The initial refrigerant state of each of the plurality of refrigerant cells 23 in the heat exchanger 10 is set to either liquid refrigerant 14a, gas refrigerant 14b, or gas-liquid mixed refrigerant 14c.

[0149] When solving equation (19) in step S13, equation (19) is solved according to the state of the refrigerant cell 23. Specifically, in a cell where the refrigerant state is liquid refrigerant 14a (or gas refrigerant 14b), a fixed value is input for the dryness fraction x in equation (19), or equation (19) is iteratively calculated by a brute force method or the like until the optimized solution for the dryness fraction x approximately matches the fixed value. In a cell where the refrigerant state is gas-liquid mixed refrigerant 14c, the temperature T w A fixed value is entered for w Equation (19) is iteratively calculated until the optimized solution of approximately matches the fixed value.

[0150] In step S13, the temperature T w Alternatively, the quality fraction x is updated. If the calculation does not converge in step S14, the refrigerant state of each of the plurality of refrigerant cells 23 is updated in step S11.

[0151] Specifically, in the cell where the refrigerant state is liquid refrigerant 14a, the updated temperature T w is equal to or higher than a predetermined threshold value (for example, evaporation temperature), the refrigerant state is updated to gas-liquid mixed refrigerant 14c.

[0152] In the cell where the refrigerant state is the gas-liquid mixture refrigerant 14c, the updated quality fraction x is equal to or exceeds a predetermined threshold value (for example, x SL ), the refrigerant state is updated to liquid refrigerant 14a. SG ) or more, the refrigerant state is updated to gas refrigerant 14b.

[0153] In the cell where the refrigerant state is gas refrigerant 14b, the updated temperature T wis equal to or lower than a predetermined threshold value (for example, the condensation temperature), the refrigerant state is updated to gas-liquid mixed refrigerant 14c.

[0154] As described above, the refrigerant state of each refrigerant cell 23 is updated in step S11, and equation (19) is solved based on the updated refrigerant state.

[0155] The refrigerant state of each refrigerant cell 23 may be stored in a predetermined temporary memory or the like when the initial refrigerant state is set and when the refrigerant state is updated. In steps S11 and S13, the refrigerant state of each refrigerant cell 23 can be determined by referring to the data in the temporary memory. Alternatively, in steps S11 and S13, the refrigerant state of each refrigerant cell 23 may be determined by referring to the dryness fraction x in the variable matrix T, etc.

[0156] In this way, the anomaly detection method according to the second embodiment of the present disclosure derives the temperature distribution through iterative calculations while sequentially updating the physical property values. This allows the temperature distribution to be analyzed using linear equations at each step of the iterative calculations, even when the physical property values ​​change due to temperature changes in two-phase flow. Therefore, the anomaly detection method according to the second embodiment of the present disclosure can simplify the calculations and reduce the amount of calculations.

[0157] Furthermore, some of the physical property values ​​are calculated in advance using CAE or the like and stored in the storage unit 76, and are acquired from the storage unit 76 when the physical property values ​​are updated sequentially. This simplifies the updating of physical property values, even for those physical property values ​​whose temperature dependency is complicated to obtain from a theoretical formula. Furthermore, when updating the physical property values, interpolating the data acquired from the storage unit 76 can prevent the temperature from diverging.

[0158] In the anomaly detection method according to the second embodiment of the present disclosure, the variable matrix T is expanded to include the quality fraction x of the refrigerant 14. Whether the refrigerant 14 in each refrigerant cell 23 is in a single-phase state or a two-phase state is successively updated. For a refrigerant cell 23 in a two-phase state, the temperature T wis treated as a known quantity, and the dryness fraction x is treated as an unknown quantity. This allows the latent heat component of the inflow enthalpy, which is nonlinear with respect to the temperature Tw, to be analyzed linearly using the dryness fraction x, thereby reducing the calculation load on the computer.

[0159] The anomaly detection method according to the second embodiment of the present disclosure may be used to detect a temperature anomaly in the heat exchanger 10, and similarly to the first embodiment, may be used to detect a physical quantity that is difficult to measure with a sensor (for example, a flow rate m a and m w ) The abnormality detection method according to the second embodiment of the present disclosure can also be applied to the fin-tube heat exchanger 10a in the same manner.

[0160] At least some of the functions of the abnormality detection device 70 in the above-described embodiment may be configured as hardware or software. If configured as software, a program that realizes at least some of the functions of the abnormality detection device 70 may be stored on a recording medium such as a flexible disk or CD-ROM, and may be read and executed by a computer. The recording medium is not limited to removable media such as magnetic disks and optical disks, but may also be fixed recording media such as hard disk drives and memories.

[0161] In addition, a program that realizes at least a part of the functions of the anomaly detection device 70 may be distributed via a communication line (including wireless communication) such as the Internet. Furthermore, the program may be encrypted, modulated, or compressed and distributed via a wired line or wireless line such as the Internet, or stored on a recording medium.

[0162] The present invention is not limited to the above-described embodiments, and may be implemented in various ways. The components may be modified and embodied without departing from the spirit of the invention. By appropriately combining the multiple components described above, various inventions can be created. For example, a configuration in which some components are deleted from all the components shown in each embodiment may also be considered. Furthermore, the components described in different embodiments may be combined as appropriate.

[0163] [Note] [Item 1] a step of inputting known data, including information acquired by a sensor provided for a heat exchanger, into a numerical analysis model based on a thermal network method of the heat exchanger that circulates a medium and exchanges heat with the outside, to derive first unknown data relating to a temperature in the heat exchanger and second unknown data relating to a flow rate of the medium; and comparing the second unknown data with a predetermined threshold value to detect an abnormality in the heat exchanger. Anomaly detection methods. [Item 2] The method further includes a step of acquiring a temperature of an inlet boundary of the heat exchanger and a temperature of an outlet boundary of the heat exchanger by the sensor; The known data includes a temperature of an inlet boundary of the heat exchanger and a temperature of an outlet boundary of the heat exchanger. Item 1. The anomaly detection method according to item 1. [Item 3] the medium includes air and a refrigerant; the derived second unknown data includes at least one of the air flow rate or the refrigerant flow rate; The abnormality of the heat exchanger includes at least one of an abnormality in the flow rate of the air or an abnormality in the flow rate of the refrigerant. Item 2. The anomaly detection method according to item 2. [Item 4] the derived first unknown data includes at least one of a temperature of the air, a temperature of the refrigerant, or a temperature of the heat exchanger piping; detecting a temperature abnormality in the heat exchanger based on at least one of the temperature of the air, the temperature of the refrigerant, and the temperature of a pipe of the heat exchanger; Item 3. The anomaly detection method according to item 3. [Item 5] setting a tentative value for the second unknown data; inputting the provisional value into the numerical analysis model to calculate either the temperature of the inlet boundary of the heat exchanger or the temperature of the outlet boundary of the heat exchanger; comparing the calculated one value with a value acquired by the sensor; and if the calculated value and the value acquired by the sensor substantially match, deriving the provisional value as the value of the second unknown data. 5. The anomaly detection method according to any one of items 2 to 4. [Item 6] The method further comprises a step of solving a simultaneous equation including the known data for the first unknown data and the second unknown data, the numerical analysis model includes a plurality of piping cells formed by dividing a piping through which a refrigerant flows in the heat exchanger in the flow direction of the refrigerant, a plurality of air cells formed by dividing an air layer around the heat exchanger in the flow direction of the refrigerant, and a plurality of refrigerant cells formed by dividing the refrigerant in the flow direction, The simultaneous equations include a plurality of first equations related to heat exchange in the plurality of air cells, a plurality of second equations related to heat exchange in the plurality of piping cells, and a plurality of third equations related to heat exchange in the plurality of refrigerant cells. 6. The anomaly detection method according to any one of items 2 to 5. [Item 7] The method further comprises a step of solving a simultaneous equation including the known data for the first unknown data and the second unknown data, The numerical analysis model includes a plurality of tube cells formed by dividing a tube through which a refrigerant flows in the heat exchanger in the flow direction of the refrigerant, a plurality of fin group cells each including one or more fins arranged on the tube along the flow direction of the refrigerant, a plurality of air cells formed by dividing an air layer around the heat exchanger in the flow direction of the refrigerant, and a plurality of refrigerant cells formed by dividing the refrigerant in the flow direction, the simultaneous equations include a plurality of first equations relating to heat exchange in the plurality of air cells, a plurality of second equations relating to heat exchange in the plurality of fin group cells, a plurality of third equations relating to heat exchange in the plurality of tube cells, and a plurality of fourth equations relating to heat exchange in the plurality of refrigerant cells. 6. The anomaly detection method according to any one of items 2 to 5. [Item 8] The method further comprises a step of solving the simultaneous equations using at least one of an LU (Lower-Upper) decomposition method, an SOR (Successive Over-Relaxation) method, a conjugate gradient method, or a Krylov subspace method. Item 8. The anomaly detection method according to Item 6 or 7. [Item 9] The method further comprises a step of calculating the second unknown data from the simultaneous equations using at least one of a Newton-Raphson algorithm, a BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm, a Nelder-Mead algorithm, a Powell algorithm, a brute force search algorithm, a four-dimensional variational method, or a Kalman filter. Item 9. The anomaly detection method according to any one of Items 6 to 8. [Item 10] A procedure for generating a numerical analysis model of a heat exchanger using the thermal network method; a step of solving a simultaneous equation including known data including information acquired by a sensor and first unknown data including information on heat in the heat exchanger based on the numerical analysis model, for the first unknown data; a procedure for comparing the solved first unknown data with a predetermined threshold value to determine whether an abnormality exists; the numerical analysis model includes a plurality of refrigerant cells formed by dividing the refrigerant flowing through the heat exchanger in a flow direction, The plurality of refrigerant cells include at least one of a plurality of single-phase refrigerant cells in which the refrigerant is in a gas or liquid state, or a plurality of two-phase refrigerant cells in which the refrigerant is in a gas-liquid mixed state, the known data includes a plurality of dryness fractions of the plurality of single-phase refrigerant cells and a plurality of temperatures of the plurality of two-phase refrigerant cells; the first unknown data solved includes a plurality of temperatures of the plurality of single-phase refrigerant cells and a plurality of quality fractions of the plurality of two-phase refrigerant cells; Anomaly detection methods. [Item 11] The plurality of refrigerant cells include at least one of a plurality of single-phase refrigerant cells in which the refrigerant is in a gas or liquid state, or a plurality of two-phase refrigerant cells in which the refrigerant is in a gas-liquid mixed state, the known data includes a plurality of dryness fractions of the plurality of single-phase refrigerant cells and a plurality of temperatures of the plurality of two-phase refrigerant cells; the first unknown data solved includes a plurality of temperatures of the plurality of single-phase refrigerant cells and a plurality of quality fractions of the plurality of two-phase refrigerant cells; 10. The anomaly detection method according to any one of items 6 to 9. [Item 12] the simultaneous equations include a nonlinear equation having one or more coefficients that vary with temperature in the heat exchanger; The solution of the nonlinear equation involves: a deriving step of solving a linear equation to derive the temperature; an updating step of updating the coefficient based on the temperature derived in the derivation step; generating the linear equations using the coefficients; and Item 12. The anomaly detection method according to Item 10 or 11. [Item 13] The updating step includes: an acquiring step of acquiring a plurality of data from a data set that records the relationship between the temperature and the coefficient; an interpolation step of interpolating the plurality of data to derive a value of the coefficient according to the temperature derived in the derivation step, Item 13. The anomaly detection method according to item 12. [Item 14] a calculation unit that inputs known data, including information acquired by a sensor provided for a heat exchanger, into a numerical analysis model based on a thermal network method of the heat exchanger that circulates a medium and exchanges heat with the outside, and derives first unknown data related to a temperature in the heat exchanger and second unknown data related to a flow rate of the medium; a determination unit that compares the second unknown data with a predetermined threshold value to detect an abnormality in the heat exchanger, Anomaly detection device. [Explanation of symbols]

[0164] 1 air conditioning equipment, 2 evaporator, 3 compressor, 4 condenser, 5 expansion valve, 10, 10a heat exchanger, 11 insulated pipe, 12, 41 tube, 13 air, 14 refrigerant, 14a liquid refrigerant, 14b gas refrigerant, 14c gas-liquid mixture refrigerant, 20, 20a unit structure, 21, 21a, 21b air cell, 22, 22a, 22b, 22c metal cell, 23, 23a, 23b refrigerant cell, 30 thermal circuit, 31, 31a, 31b, 32, 32a, 32b, 32c, 33, 33a, 33b, 61, 61a, 61b, 61c, 62, 62a, 62b, 62c calculation point, 34, 35, 36, 37, 38, 53, 54 heat transfer, 42 Fin, 43 Repeated structure, 51, 51a, 51b, 51c Fin cell, 52, 52a, 52b, 52c Tube cell, 70 Anomaly detection device, 71 Input data acquisition unit, 72 Model generation unit, 73 Calculation unit, 74 Determination unit, 75 Result output unit, 76 Memory unit

Claims

1. a step of inputting known data, including information acquired by a sensor provided for a heat exchanger, into a numerical analysis model based on a thermal network method of the heat exchanger that circulates a medium and exchanges heat with the outside, to derive first unknown data relating to a temperature inside the heat exchanger and second unknown data relating to a flow rate of the medium; and comparing the second unknown data with a predetermined threshold value to detect an abnormality in the heat exchanger. Anomaly detection methods.

2. The method further includes a step of acquiring a temperature of an inlet boundary of the heat exchanger and a temperature of an outlet boundary of the heat exchanger by the sensor; The known data includes a temperature of an inlet boundary of the heat exchanger and a temperature of an outlet boundary of the heat exchanger. The anomaly detection method according to claim 1 .

3. the medium includes air and a refrigerant; the derived second unknown data includes at least one of a flow rate of the air or a flow rate of the refrigerant; The abnormality of the heat exchanger includes at least one of an abnormality in the flow rate of the air or an abnormality in the flow rate of the refrigerant. The anomaly detection method according to claim 2 .

4. the derived first unknown data includes at least one of a temperature of the air, a temperature of the refrigerant, or a temperature of piping of the heat exchanger; detecting a temperature abnormality in the heat exchanger based on at least one of a temperature of the air, a temperature of the refrigerant, or a temperature of a pipe of the heat exchanger; The anomaly detection method according to claim 3 .

5. setting a tentative value for the second unknown data; inputting the provisional value into the numerical analysis model to calculate either the temperature of the inlet boundary of the heat exchanger or the temperature of the outlet boundary of the heat exchanger; comparing the calculated one value with a value acquired by the sensor; and if the calculated value and the value acquired by the sensor substantially match, deriving the provisional value as the value of the second unknown data. The anomaly detection method according to claim 2 .

6. The method further comprises a step of solving a simultaneous equation including the known data for the first unknown data and the second unknown data, the numerical analysis model includes a plurality of piping cells formed by dividing a piping through which a refrigerant flows in the heat exchanger in the flow direction of the refrigerant, a plurality of air cells formed by dividing an air layer around the heat exchanger in the flow direction of the refrigerant, and a plurality of refrigerant cells formed by dividing the refrigerant in the flow direction, the simultaneous equations include a plurality of first equations related to heat exchange in the plurality of air cells, a plurality of second equations related to heat exchange in the plurality of piping cells, and a plurality of third equations related to heat exchange in the plurality of refrigerant cells. The anomaly detection method according to claim 2 .

7. The method further comprises a step of solving a simultaneous equation including the known data for the first unknown data and the second unknown data, The numerical analysis model includes a plurality of tube cells formed by dividing a tube through which a refrigerant flows in the heat exchanger in the flow direction of the refrigerant, a plurality of fin group cells each including one or more fins arranged on the tube along the flow direction of the refrigerant, a plurality of air cells formed by dividing an air layer around the heat exchanger in the flow direction of the refrigerant, and a plurality of refrigerant cells formed by dividing the refrigerant in the flow direction, the simultaneous equations include a plurality of first equations relating to heat exchange in the plurality of air cells, a plurality of second equations relating to heat exchange in the plurality of fin group cells, a plurality of third equations relating to heat exchange in the plurality of tube cells, and a plurality of fourth equations relating to heat exchange in the plurality of refrigerant cells. The anomaly detection method according to claim 2 .

8. The method further comprises a procedure of solving the simultaneous equations using at least one of an LU (Lower-Upper) decomposition method, an SOR (Successive Over-Relaxation) method, a conjugate gradient method, or a Krylov subspace method. The anomaly detection method according to claim 6 .

9. The method further comprises a step of calculating the second unknown data from the simultaneous equations using at least one of a Newton-Raphson method, a BFGS (Broyden-Fletcher-Goldfarb-Shanno) method, a Nelder-Mead method, a Powell method, a brute force search method, a four-dimensional variational method, or a Kalman filter. The anomaly detection method according to claim 6 .

10. A procedure for generating a numerical analysis model of a heat exchanger using the thermal network method; a step of solving a simultaneous equation including known data including information acquired by a sensor and first unknown data including information on heat in the heat exchanger based on the numerical analysis model, for the first unknown data; a procedure for comparing the solved first unknown data with a predetermined threshold value to determine whether an abnormality exists; the numerical analysis model includes a plurality of refrigerant cells formed by dividing the refrigerant flowing through the heat exchanger in a flow direction, The plurality of refrigerant cells include at least one of a plurality of single-phase refrigerant cells in which the refrigerant is in a gas or liquid state, or a plurality of two-phase refrigerant cells in which the refrigerant is in a gas-liquid mixed state, the known data includes a plurality of dryness fractions of the plurality of single-phase refrigerant cells and a plurality of temperatures of the plurality of two-phase refrigerant cells; the first unknown data solved for includes a plurality of temperatures of the plurality of single-phase refrigerant cells and a plurality of quality fractions of the plurality of two-phase refrigerant cells; Anomaly detection methods.

11. the simultaneous equations include a nonlinear equation having one or more coefficients that vary with temperature in the heat exchanger; The solution of the nonlinear equation involves: a deriving step of solving a linear equation to derive the temperature; an updating step of updating the coefficient based on the temperature derived in the derivation step; generating the linear equations using the coefficients; and The anomaly detection method according to claim 10.

12. The updating step includes: an acquiring step of acquiring a plurality of data from a data set that records the relationship between the temperature and the coefficient; an interpolation step of interpolating the plurality of data to derive a value of the coefficient according to the temperature derived in the derivation step, The anomaly detection method according to claim 11.

13. a calculation unit that inputs known data, including information acquired by a sensor provided for a heat exchanger, into a numerical analysis model based on a thermal network method of the heat exchanger that circulates a medium and exchanges heat with the outside, and derives first unknown data related to a temperature inside the heat exchanger and second unknown data related to a flow rate of the medium; a determination unit that compares the second unknown data with a predetermined threshold value to detect an abnormality in the heat exchanger. Anomaly detection device.

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