A CHF phenomenon identification method, system and medium based on support vector machine

Abstract

The present invention discloses a CHF phenomenon identification method, system, and medium based on a support vector machine. The method includes: obtaining measurement point data of the experimental body fluid outlet temperature, pressure, and the experimental body outer wall temperature; converting and classifying the experimental body outer wall temperature measurement point data into temperature change rate data to obtain temperature change rate data before and after the CHF occurs; using a support vector machine algorithm to perform supervised learning on the temperature change rate data before and after the CHF occurs to obtain a CHF intelligent judgment model based on the support vector machine; inputting all the experimental body outer wall temperature measurement point data, after temperature change rate conversion, into the support vector machine-based CHF intelligent judgment model in real time to perform a preliminary CHF judgment; and further performing a CHF judgment based on the experimental body fluid outlet temperature and the experimental body fluid outlet pressure. The present invention can avoid misjudgment and omission of the CHF phenomenon operating point and automatically provide the operating point.

Description

Technical Field

[0001] The present invention relates to the field of thermal hydraulic experimental research, and in particular to a CHF phenomenon identification method, system and medium based on a support vector machine. Background Art

[0002] Critical heat flux experiments are a common safety test in thermal hydraulic research. The vapor content and heat flux at the critical heat flux (CHF) operating point vary for different test bodies, flow rates, and pressures. Therefore, experiments often involve intermittently increasing the heat flux of the test body. After the CHF occurs, heat transfer deteriorates, preventing heat from being promptly removed from the heated wall, causing the test body's outer wall temperature to soar. Based on empirical evidence, CHF occurrence is detected, and the heating power is subsequently reduced. However, during the experiment, unusual flow rate fluctuations can cause an anomalous spike in outer wall temperature, leading to misjudgment. Alternatively, in some small test bodies, the wall temperature can rise too rapidly, and untimely observation can lead to excessive temperature increases and burnout of the test body.

[0003] In view of this, this application is hereby filed. Summary of the Invention

[0004] The present invention aims to provide a support vector machine-based CHF phenomenon identification method, system, and medium. After fully understanding the characteristics of critical heat flux density experiments and their experimental apparatus, a support vector machine-based CHF phenomenon identification technology was designed. This technology can effectively determine the CHF phenomenon operating point, avoiding both misjudgment and omission of CHF phenomenon operating points, and automatically presenting the operating point. The present invention solves the problems of experimental data anomalies caused by misjudgment of operating points and experimental body burnout caused by omission of operating points during thermal-hydraulic critical heat flux density experiments. This technology can effectively improve the accuracy of critical heat flux density experimental data and reduce experimental body burnout during critical heat flux density experiments.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a CHF phenomenon identification method based on a support vector machine, the method comprising:

[0007] Obtaining the measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature;

[0008] The temperature change rate data of the outer wall temperature of the experimental body are converted and classified to obtain the temperature change rate data before and after the CHF occurs;

[0009] The support vector machine algorithm is used to conduct supervised learning on the temperature change rate data before and after the CHF occurs, and a CHF intelligent judgment model based on the support vector machine is obtained. The CHF intelligent judgment model based on the support vector machine can automatically judge the CHF phenomenon.

[0010] The temperature change rate-converted data of all the experimental body's outer wall temperature measurement points are input into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain preliminary judgment results;

[0011] If the preliminary judgment result is a preliminary CHF phenomenon, further CHF judgment is performed in combination with the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point.

[0012] Furthermore, the measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature are obtained, including:

[0013] A first temperature measuring point and a pressure measuring point are set at the fluid outlet of the experimental body, and measurement data of the fluid outlet temperature of the experimental body and measurement data of the fluid outlet pressure of the experimental body are obtained through the first temperature measuring point and the pressure measuring point respectively;

[0014] A second temperature measuring point is set on the outer wall of the experimental body, and the measurement point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

[0015] Furthermore, the temperature change rate data of the outer wall temperature of the experimental body is converted and classified to obtain the temperature change rate data before and after the CHF occurs, including:

[0016] Converting the measurement point data of the outer wall temperature of the experimental body into multiple temperature change rates to obtain the temperature change rates of the multiple measurement points, including: multiple temperature change rates △T11, △T12, △T13, ..., △T1n of the first measurement point, multiple temperature change rates △T21, △T22, △T23, ..., △T2n, ... of the second measurement point, and multiple temperature change rates △Tm1, △Tm2, △Tm3, ..., △Tmn of the mth measurement point;

[0017] According to the temperature change rates of multiple measuring points, the temperature change rates of the outer wall temperature of the experimental body corresponding to the measuring points where the CHF phenomenon occurs are classified to obtain the temperature change rate data before and after the CHF occurs.

[0018] Furthermore, the expression of the temperature change rate △Tmn is:

[0019]

[0020] in, Indicates the temperature of the mth measuring point at the current moment, Indicates the temperature at the mth measurement point before the nth time step, t i Indicates the current time, t i-n Indicates the time of the nth time step forward.

[0021] Furthermore, if the preliminary judgment result is a preliminary CHF phenomenon, further CHF judgment is performed in combination with the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point, including:

[0022] If the preliminary judgment result is preliminary CHF phenomenon, that is, all temperature change rates of the outer wall temperature of the same experimental body are judged to be CHF phenomenon, then the measured experimental body fluid outlet pressure is input into the water / water vapor physical property calculation program to calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure. sat ;

[0023] Based on the absolute value temperature judgment method, judge abs(T out -T sat ) is less than or equal to the preset value to obtain the judgment result; where abs() is the absolute value function, T out The outlet temperature of the experimental body fluid is obtained by measurement;

[0024] If the judgment result is no, the CHF phenomenon has not occurred; if the judgment result is yes, the final CHF phenomenon has occurred.

[0025] Furthermore, the method further includes: obtaining a CHF operating time point based on the final CHF phenomenon, specifically:

[0026] The measuring point that first satisfies the condition that all temperature change rates of the outer wall temperature of the same experimental body are judged as CHF is the measuring point where CHF occurs first; the moment when the temperature change rate of this measuring point first satisfies CHF in this judgment is the CHF working condition moment point.

[0027] In a second aspect, the present invention further provides a CHF phenomenon recognition system based on a support vector machine, the system comprising:

[0028] An acquisition unit, used to acquire measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature;

[0029] The conversion and classification unit is used to convert and classify the temperature change rate data of the outer wall temperature of the experimental body into temperature change rate data, and obtain the temperature change rate data before and after the CHF occurs;

[0030] The model training unit is used to perform supervised learning on the temperature change rate data before and after the CHF occurs using a support vector machine algorithm to obtain a CHF intelligent judgment model based on the support vector machine; the CHF intelligent judgment model based on the support vector machine can automatically judge the CHF phenomenon;

[0031] The first judgment unit is used to input the temperature change rate converted data of all the experimental body outer wall measurement points into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain a preliminary judgment result;

[0032] The second judgment unit is used to further judge the CHF phenomenon by combining the test body fluid outlet temperature and the test body fluid outlet pressure if the preliminary judgment result is a preliminary CHF phenomenon, so as to obtain a final CHF operating point.

[0033] Furthermore, the execution process of the acquisition unit is specifically as follows:

[0034] A first temperature measuring point and a pressure measuring point are set at the fluid outlet of the experimental body, and measurement data of the fluid outlet temperature of the experimental body and measurement data of the fluid outlet pressure of the experimental body are obtained through the first temperature measuring point and the pressure measuring point respectively;

[0035] A second temperature measuring point is set on the outer wall of the experimental body, and the measurement point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

[0036] Furthermore, the transformation and classification units include:

[0037] The conversion subunit is used to convert the measurement point data of the outer wall temperature of the experimental body into multiple temperature change rates to obtain the temperature change rates of the multiple measurement points, including: multiple temperature change rates △T11, △T12, △T13, ..., △T1n of the first measurement point, multiple temperature change rates △T21, △T22, △T23, ..., △T2n, ... of the second measurement point, and multiple temperature change rates △Tm1, △Tm2, △Tm3, ..., △Tmn of the mth measurement point;

[0038] The classification subunit is used to classify the temperature change rate of the outer wall temperature of the experimental body corresponding to the measuring point where the CHF phenomenon occurs according to the temperature change rate of multiple measuring points, and obtain the temperature change rate data before and after the CHF occurs.

[0039] Furthermore, the expression of the temperature change rate △Tmn is:

[0040]

[0041] in, Indicates the temperature of the mth measuring point at the current moment, Indicates the temperature at the mth measurement point before the nth time step, t i Indicates the current time, t i-n Indicates the time of the nth time step forward.

[0042] Furthermore, the second judgment unit includes:

[0043] The saturation temperature calculation subunit is used to input the measured experimental body fluid outlet pressure into the water / water vapor physical property calculation program if the preliminary judgment result is the preliminary CHF phenomenon, that is, all temperature change rates of the outer wall temperature of the same experimental body are judged to be CHF phenomena, and calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure. sat ;

[0044] The final judgment subunit is used to judge abs(T out -T sat ) is less than or equal to the preset value to obtain the judgment result; where abs() is the absolute value function, T out is the measured outlet temperature of the experimental body fluid; if the judgment result is no, the CHF phenomenon does not occur; if the judgment result is yes, the final CHF phenomenon occurs.

[0045] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned CHF phenomenon identification method based on a support vector machine is implemented.

[0046] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned CHF phenomenon identification method based on a support vector machine when executed by a processor.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] The present invention provides a support vector machine-based CHF phenomenon identification method, system, and medium, which can effectively determine the CHF phenomenon operating point, thereby avoiding both misjudgment and omission of the CHF phenomenon operating point, and automatically presenting the operating point. The present invention solves the problem of experimental data anomalies caused by misjudgment of the operating point during thermal hydraulic critical heat flux experiments, as well as the problem of experimental body burnout caused by omission of the operating point. It can effectively improve the accuracy of critical heat flux experimental data and reduce experimental body burnout during critical heat flux experiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0050] Figure 1 This is a schematic diagram of the change of the outer wall temperature of the experimental body over time under typical CHF phenomenon conditions;

[0051] Figure 2 This is a flow chart of a CHF phenomenon identification method based on support vector machine of the present invention;

[0052] Figure 3 A detailed flow chart of a CHF phenomenon identification method based on a support vector machine according to the present invention;

[0053] Figure 4 This is a structural block diagram of a CHF phenomenon recognition system based on support vector machine in the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0055] like Figure 1 As shown in the figure, during the critical heat flux experiment, the temperatures of multiple outer wall temperature measurement points of the experimental body will rise abnormally due to the disturbance of the flow rate. Especially under high heat flux density, it is easy to cause the experimenters to misjudge the phenomenon during the experiment, resulting in the occurrence of abnormal operating points in the experiment.

[0056] Therefore, the present invention designs a CHF phenomenon identification method based on a support vector machine. By collecting the outlet temperature and pressure of the experimental body fluid as the activation conditions of the CHF intelligent judgment algorithm based on the support vector machine, and taking the outer wall temperature of the experimental body before and after the high frame rate CHF phenomenon as input, the CHF intelligent judgment algorithm based on the support vector machine is trained to obtain a CHF intelligent judgment model based on the support vector machine with the ability to automatically judge the CHF phenomenon.

[0057] By setting temperature measurement points on the outer wall of the experimental body and temperature and pressure measurement points at the fluid outlet of the experimental body for real-time data acquisition, the fluid outlet temperature, pressure, and outer wall temperature of the experimental body are obtained as inputs to determine the CHF phenomenon in real time and accurately determine the operating point of the thermal hydraulic critical heat flux density experiment.

[0058] The present invention solves the problem of abnormal experimental data caused by misjudgment of the operating point during the thermal hydraulic critical heat flux density experiment and the problem of burning of the experimental body caused by missed judgment of the operating point. It can effectively improve the accuracy of critical heat flux density experimental data and reduce the burning of the experimental body during the critical heat flux density experiment.

[0059] Example 1

[0060] like Figure 2 and Figure 3 As shown, the present invention is a CHF phenomenon identification method based on support vector machine, the method comprising:

[0061] Obtaining the measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature;

[0062] The temperature change rate data of the outer wall temperature of the experimental body are converted and classified to obtain the temperature change rate data before and after the CHF occurs;

[0063] The support vector machine algorithm is used to conduct supervised learning on the temperature change rate data before and after the CHF occurs, and a CHF intelligent judgment model based on the support vector machine is obtained. The CHF intelligent judgment model based on the support vector machine can automatically judge the CHF phenomenon.

[0064] The temperature change rate-converted data of all the experimental body's outer wall temperature measurement points are input into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain preliminary judgment results;

[0065] If the preliminary judgment result is a preliminary CHF phenomenon, further CHF judgment is performed in combination with the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point.

[0066] In this embodiment, obtaining the measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure, and the test body outer wall temperature includes:

[0067] A first temperature measuring point and a pressure measuring point are set at the fluid outlet of the experimental body, and measurement data of the fluid outlet temperature of the experimental body and measurement data of the fluid outlet pressure of the experimental body are obtained through the first temperature measuring point and the pressure measuring point respectively;

[0068] A second temperature measuring point is set on the outer wall of the experimental body, and the measurement point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

[0069] In this embodiment, the temperature change rate data of the outer wall temperature of the experimental body is converted and classified to obtain the temperature change rate data before and after the CHF occurs, including:

[0070] The measurement point data of the outer wall temperature of the experimental body are converted into multiple temperature change rates to obtain the temperature change rates of the multiple measurement points, including: multiple temperature change rates △T11, △T12, △T13, ..., △T1n of the first measurement point, multiple temperature change rates △T21, △T22, △T23, ..., △T2n, ... of the second measurement point, and multiple temperature change rates △Tm1, △Tm2, △Tm3, ..., △Tmn of the mth measurement point; wherein the expression of the temperature change rate △Tmn is:

[0071]

[0072] in, Indicates the temperature of the mth measuring point at the current moment, Indicates the temperature at the mth measurement point before the nth time step, t i Indicates the current time, t i-n Indicates the time of the nth time step forward.

[0073] According to the temperature change rates of multiple measuring points, the temperature change rates of the outer wall temperature of the experimental body corresponding to the measuring points where the CHF phenomenon occurs are classified to obtain the temperature change rate data before and after the CHF occurs.

[0074] In this embodiment, the temperature change rate-converted data of all the experimental body's outer wall temperature measurement points are input into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment, and obtain preliminary judgment results, including:

[0075] The temperature change rate-converted data of all the experimental body's outer wall temperature measurement points are input into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain preliminary judgment results;

[0076] If the preliminary judgment result is negative, that is, CHF phenomenon has not occurred, then re-judgment is performed;

[0077] If the preliminary judgment result is yes, that is, CHF phenomenon has occurred, then proceed to the next step.

[0078] In this embodiment, if the preliminary judgment result is a preliminary CHF phenomenon, further CHF judgment is performed in combination with the experimental body fluid outlet temperature and the experimental body fluid outlet pressure to obtain the final CHF operating point, including:

[0079] If the preliminary judgment result is preliminary CHF phenomenon, that is, all temperature change rates of the outer wall temperature of the same experimental body are judged to be CHF phenomenon, then the measured experimental body fluid outlet pressure is input into the water / water vapor physical property calculation program to calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure.sat ;

[0080] Based on the absolute value temperature judgment method, judge abs(T out -T sat ) is less than or equal to the preset value to obtain the judgment result; where abs() is the absolute value function, T out The outlet temperature of the experimental body fluid is obtained by measurement;

[0081] If the judgment result is no, the CHF phenomenon has not occurred; if the judgment result is yes, the final CHF phenomenon has occurred.

[0082] It should be noted that the water / water vapor property calculation program is a prior art and will not be described in detail here.

[0083] In this embodiment, the method further includes: obtaining a CHF operating time point based on the final CHF phenomenon, specifically:

[0084] The measuring point that first satisfies the condition that all temperature change rates of the outer wall temperature of the same experimental body are judged as CHF is the measuring point where CHF occurs first; the moment when the temperature change rate of this measuring point first satisfies CHF in this judgment is the CHF working condition moment point.

[0085] The method of the present invention can effectively judge the CHF phenomenon operating point, so that it can avoid both misjudgment of the CHF phenomenon operating point and omission of the CHF phenomenon operating point, and automatically gives the operating point, effectively improving the accuracy of critical heat flux density experimental data and reducing the burning of the experimental body.

[0086] Example 2

[0087] like Figure 4 As shown, the difference between this embodiment and embodiment 1 is that this embodiment provides a CHF phenomenon identification system based on a support vector machine. This system uses the CHF phenomenon identification method based on a support vector machine in embodiment 1. The system corresponds to the CHF phenomenon identification method based on a support vector machine in embodiment 1 in a one-to-one functional manner. The system includes:

[0088] An acquisition unit, used to acquire measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature;

[0089] The conversion and classification unit is used to convert and classify the temperature change rate data of the outer wall temperature of the experimental body into temperature change rate data, and obtain the temperature change rate data before and after the CHF occurs;

[0090] The model training unit is used to perform supervised learning on the temperature change rate data before and after the CHF occurs using a support vector machine algorithm to obtain a CHF intelligent judgment model based on the support vector machine; the CHF intelligent judgment model based on the support vector machine can automatically judge the CHF phenomenon;

[0091] The first judgment unit is used to input the temperature change rate converted data of all the experimental body outer wall measurement points into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain a preliminary judgment result;

[0092] The second judgment unit is used to further judge the CHF phenomenon by combining the test body fluid outlet temperature and the test body fluid outlet pressure if the preliminary judgment result is a preliminary CHF phenomenon, so as to obtain a final CHF operating point.

[0093] As a further implementation, the execution process of the acquisition unit is specifically as follows:

[0094] A first temperature measuring point and a pressure measuring point are set at the fluid outlet of the experimental body, and measurement data of the fluid outlet temperature of the experimental body and measurement data of the fluid outlet pressure of the experimental body are obtained through the first temperature measuring point and the pressure measuring point respectively;

[0095] A second temperature measuring point is set on the outer wall of the experimental body, and the measurement point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

[0096] As a further implementation, the transformation and classification unit includes:

[0097] The conversion subunit is used to convert the measurement point data of the outer wall temperature of the experimental body into multiple temperature change rates to obtain the temperature change rates of the multiple measurement points, including: multiple temperature change rates △T11, △T12, △T13, ..., △T1n of the first measurement point, multiple temperature change rates △T21, △T22, △T23, ..., △T2n, ... of the second measurement point, and multiple temperature change rates △Tm1, △Tm2, △Tm3, ..., △Tmn of the mth measurement point;

[0098] The classification subunit is used to classify the temperature change rate of the outer wall temperature of the experimental body corresponding to the measuring point where the CHF phenomenon occurs according to the temperature change rate of multiple measuring points, and obtain the temperature change rate data before and after the CHF occurs.

[0099] As a further implementation, the expression of the temperature change rate ΔTmn is:

[0100]

[0101] in, Indicates the temperature of the mth measuring point at the current moment, Indicates the temperature at the mth measurement point before the nth time step, t i Indicates the current time, t i-n Indicates the time of the nth time step forward.

[0102] As a further implementation, the second judgment unit includes:

[0103] The saturation temperature calculation subunit is used to input the measured experimental body fluid outlet pressure into the water / water vapor physical property calculation program if the preliminary judgment result is the preliminary CHF phenomenon, that is, all temperature change rates of the outer wall temperature of the same experimental body are judged to be CHF phenomena, and calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure. sat ;

[0104] The final judgment subunit is used to judge abs(T out -T sat ) is less than or equal to the preset value to obtain the judgment result; where abs() is the absolute value function, T out is the measured outlet temperature of the experimental body fluid; if the judgment result is no, the CHF phenomenon does not occur; if the judgment result is yes, the final CHF phenomenon occurs.

[0105] The execution process of each unit can be performed according to the process steps of the CHF phenomenon identification method based on support vector machine in Example 1, and will not be described in detail in this embodiment.

[0106] At the same time, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the above-mentioned CHF phenomenon identification method based on a support vector machine is implemented.

[0107] At the same time, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned CHF phenomenon identification method based on a support vector machine.

[0108] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A CHF phenomenon identification method based on support vector machine, characterized in that: The method includes: Obtaining the measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature; Convert and classify the temperature change rate data of the outer wall temperature of the experimental body to obtain the temperature change rate data before and after the CHF occurs; A support vector machine algorithm is used to perform supervised learning on the temperature change rate data before and after the occurrence of CHF, thereby obtaining a CHF intelligent judgment model based on the support vector machine; the CHF intelligent judgment model based on the support vector machine can automatically judge the CHF phenomenon; The measured data of the outer wall temperature of all experimental bodies after being converted into the temperature change rate are input into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain preliminary judgment results; If the preliminary judgment result is a preliminary CHF phenomenon, further CHF judgment is performed in combination with the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point.

2. A CHF phenomenon identification method based on support vector machine according to claim 1, characterized in that: Obtain the measurement point data of the test body fluid outlet temperature, test body fluid outlet pressure and test body outer wall temperature, including: Setting a first temperature measuring point and a pressure measuring point at the fluid outlet of the experimental body, and obtaining measurement data of the fluid outlet temperature of the experimental body and measurement data of the fluid outlet pressure of the experimental body through the first temperature measuring point and the pressure measuring point respectively; A second temperature measuring point is set on the outer wall of the experimental body, and the measurement point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

3. The CHF phenomenon identification method based on support vector machine according to claim 1 is characterized in that: The temperature change rate data of the outer wall temperature of the experimental body are converted and classified to obtain the temperature change rate data before and after the CHF occurs, including: Converting the measurement point data of the outer wall temperature of the experimental body into multiple temperature change rates to obtain the temperature change rates of the multiple measurement points, including: multiple temperature change rates △T11, △T12, △T13, ..., △T1n of the first measurement point, multiple temperature change rates △T21, △T22, △T23, ..., △T2n, ... of the second measurement point, and multiple temperature change rates △Tm1, △Tm2, △Tm3, ..., △Tmn of the mth measurement point; According to the temperature change rates of multiple measuring points, the temperature change rates of the outer wall temperature of the experimental body corresponding to the measuring points where the CHF phenomenon occurs are classified to obtain the temperature change rate data before and after the CHF occurs.

4. A CHF phenomenon identification method based on support vector machine according to claim 3, characterized in that: The expression of temperature change rate △Tmn is: in, Indicates the temperature of the mth measuring point at the current moment, Indicates the temperature at the mth measurement point before the nth time step, t i Indicates the current time, t i-n Indicates the time of the nth time step forward.

5. The CHF phenomenon identification method based on support vector machine according to claim 1 is characterized in that: If the preliminary judgment result is a preliminary CHF phenomenon, further CHF judgment is performed in combination with the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point, including: If the preliminary judgment result is preliminary CHF phenomenon, that is, all temperature change rates of the outer wall temperature of the same experimental body are judged to be CHF phenomenon, then the measured experimental body fluid outlet pressure is input into the water / water vapor physical property calculation program to calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure. sat ; Based on the absolute value temperature judgment method, judge abs(T out -T sat ) is less than or equal to the preset value to obtain the judgment result; where abs() is the absolute value function, T out The outlet temperature of the experimental body fluid is obtained by measurement; If the judgment result is no, the CHF phenomenon has not occurred; if the judgment result is yes, the final CHF phenomenon has occurred.

6. A CHF phenomenon identification method based on support vector machine according to claim 5, characterized in that: The method further includes: obtaining a CHF operating time point based on the final CHF phenomenon, specifically: The measuring point that first satisfies the condition that all temperature change rates of the outer wall temperature of the same experimental body are judged as CHF is the measuring point where CHF occurs first; the moment when the temperature change rate of this measuring point first satisfies CHF in this judgment is the CHF working condition moment point.

7. A CHF phenomenon recognition system based on support vector machine, characterized in that: The system includes: An acquisition unit, used to acquire measurement point data of the test body fluid outlet temperature, the test body fluid outlet pressure and the test body outer wall temperature; A conversion and classification unit, for converting and classifying the temperature change rate data of the outer wall temperature of the experimental body into temperature change rate data before and after the occurrence of CHF; a model training unit for performing supervised learning on the temperature change rate data before and after the occurrence of CHF using a support vector machine algorithm to obtain a CHF intelligent judgment model based on the support vector machine; the CHF intelligent judgment model based on the support vector machine can automatically judge the CHF phenomenon; The first judgment unit is used to input the temperature change rate converted data of the measuring points of the outer wall temperature of all experimental bodies into the CHF intelligent judgment model based on the support vector machine in real time to perform preliminary CHF judgment and obtain a preliminary judgment result; The second judgment unit is used to further perform CHF judgment based on the test body fluid outlet temperature and the test body fluid outlet pressure if the preliminary judgment result is a preliminary CHF phenomenon, so as to obtain a final CHF operating point.

8. A CHF phenomenon recognition system based on support vector machine according to claim 7, characterized in that: The second judgment unit includes: The saturation temperature calculation subunit is used to input the measured experimental body fluid outlet pressure into the water / water vapor physical property calculation program if the preliminary judgment result is the preliminary CHF phenomenon, that is, all temperature change rates of the outer wall temperature of the same experimental body are judged to be CHF phenomena, and calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure. sat ; The final judgment subunit is used to judge abs(T out -T sat ) is less than or equal to the preset value to obtain the judgment result; where abs() is the absolute value function, T out is the measured outlet temperature of the experimental body fluid; if the judgment result is no, the CHF phenomenon does not occur; if the judgment result is yes, the final CHF phenomenon occurs.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for identifying CHF phenomenon based on a support vector machine according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying CHF phenomenon based on a support vector machine according to any one of claims 1 to 6 is implemented.

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