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

Abstract

The invention discloses a CHF phenomenon identification method and system based on a support vector machine, and a medium. Comprising the steps of obtaining test point data of fluid outlet temperature and pressure of an experiment body and outer wall temperature of the experiment body; performing temperature change rate conversion and classification on the measurement point data of the outer wall temperature of the experiment body to obtain temperature change rate data before and after the CHF moment; carrying out supervised learning on the temperature change rate data before and after the CHF moment by adopting a support vector machine algorithm to obtain a CHF intelligent judgment model based on a support vector machine; data obtained after temperature change rate conversion is carried out on the measuring point data of the outer wall temperature of all the experiment bodies are input into a CHF intelligent judgment model based on a support vector machine in real time, and preliminary CHF judgment is carried out; further CHF judgment is carried out in combination with the temperature of the fluid outlet of the experiment body and the pressure of the fluid outlet of the experiment body. According to the invention, misjudgment and missed judgment of the CHF phenomenon working condition point can be avoided, and the working condition point can be automatically given.

Description

Technical Field

[0001] The 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] The critical heat flux experiment is a thermal safety criterion experiment frequently conducted in thermal hydraulic research. The steam content and heat flux at the operating point where the critical heat flux (CHF) phenomenon occurs under different experimental bodies, different flow rates, and different pressures are different. Therefore, the method of intermittently increasing the heat flux of the experimental body is often used in the experiment. After the CHF phenomenon occurs, due to the deterioration of heat transfer, the heat cannot be extracted from the heated wall in time, and the outer wall temperature of the experimental body soars. Based on experience, it is judged that CHF occurs, and then the heating power is cut off. During the experiment, abnormal fluctuations in flow rate may cause abnormal soaring of the outer wall temperature, leading to misjudgment. Or in some small experimental bodies, the wall temperature rises too fast, and the observation is not timely, resulting in excessive increase in wall temperature and burning of the experimental body.

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

[0004] The purpose of the present invention is to provide a CHF phenomenon identification method, system and medium based on a support vector machine. After fully understanding the characteristics of the critical heat flux density experiment and its experimental device, a CHF phenomenon identification technology based on a support vector machine is designed, which can effectively judge the CHF phenomenon operating point, so that it can avoid misjudgment of the CHF phenomenon operating point, avoid missed judgment of the CHF phenomenon operating point, and automatically give the operating point. The present invention solves the problem of abnormal experimental data caused by misjudgment of the operating point in the process of thermal hydraulic critical heat flux density experiment and the problem of burning of the experimental body caused by missed judgment of the operating point, and can effectively improve the accuracy of the critical heat flux density experimental data and reduce the burning of the experimental body during the critical heat flux density experiment.

[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 data of the temperature measurement points of the outer wall temperature of all experimental bodies after the temperature change rate conversion are input into the CHF intelligent judgment model based on the support vector machine in real time to make a preliminary CHF judgment and obtain the preliminary judgment result;

[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 surface of the experimental body, and the measuring 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] 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;

[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 measuring point at the nth time step before, 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 a 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, 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 obtained for 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 all temperature change rates of the outer wall temperature of the same experimental body 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 the 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 of the measurement point data of the outer wall temperature of the experimental body, and obtain the temperature change rate data before and after the CHF occurs;

[0030] The model training unit is used to use the support vector machine algorithm to perform supervised learning on the temperature change rate data before and after the CHF occurs, and 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 data of the measuring points of the outer wall temperature of all experimental bodies after the temperature change rate conversion into the CHF intelligent judgment model based on the support vector machine in real time, perform preliminary CHF judgment, and obtain preliminary judgment results;

[0032] The second judgment unit is used for further CHF judgment based on the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point if the preliminary judgment result is a preliminary CHF phenomenon.

[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 surface of the experimental body, and the measuring point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

[0036] Further, 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 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 measuring point at the nth time step before, t i Indicates the current time, t i-n Indicates the time of the nth time step forward.

[0042] Further, 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 to calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure 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. 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 The outlet temperature of the experimental body fluid is measured; 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 further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned CHF phenomenon identification method based on a support vector machine when executing the computer program.

[0046] In a fourth aspect, 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 above-mentioned CHF phenomenon identification method based on a support vector machine is implemented.

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

[0048] The present invention provides a CHF phenomenon recognition method, system and medium based on a support vector machine, which can effectively judge the CHF phenomenon operating point, so that it can avoid misjudgment of the CHF phenomenon operating point, avoid missed judgment of the CHF phenomenon operating point, and automatically give the operating point. The present invention solves the problem of abnormal experimental data caused by misjudgment of the operating point in the process of thermal hydraulic critical heat flux density experiment and the problem of burning of the experimental body caused by missed judgment of the operating point, and 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. 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 A flow chart of a CHF phenomenon identification method based on a support vector machine according to 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 The present invention is a structural block diagram of a CHF phenomenon recognition system based on a support vector machine. 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 embodiments and drawings. The exemplary embodiments of the present invention and their description 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 under the disturbance of the flow rate, especially under high heat flux density. During the experiment, it is easy to cause the experimenters to misjudge the phenomenon and cause the appearance of abnormal operating points in the experiment.

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

[0057] By setting temperature measuring points on the outer wall of the experimental body, and setting temperature measuring points and pressure measuring points at the fluid outlet of the experimental body for real-time data collection, the fluid outlet temperature, pressure and outer wall temperature of the experimental body are obtained as inputs to judge the CHF phenomenon in real time and realize accurate judgment of the critical heat flux density experimental working point of thermal hydraulics.

[0058] The present invention solves the problem of abnormal experimental data caused by misjudgment of operating points and the problem of burning of the experimental body caused by missed operating points during the critical heat flux density experiment of thermal hydraulics, and 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 a 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 data of the temperature measurement points of the outer wall temperature of all experimental bodies after the temperature change rate conversion are input into the CHF intelligent judgment model based on the support vector machine in real time to make a preliminary CHF judgment and obtain the preliminary judgment result;

[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, 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:

[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 surface of the experimental body, and the measuring 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 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 measuring point at the nth time step before, 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 data of the temperature measurement points of the outer wall temperature of all experimental bodies after the temperature change rate conversion 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 data of the temperature measurement points of the outer wall temperature of all experimental bodies after the temperature change rate conversion are input into the CHF intelligent judgment model based on the support vector machine in real time to make a preliminary CHF judgment and obtain the preliminary judgment result;

[0076] If the preliminary judgment result is no, that is, CHF phenomenon has not occurred, then re-judge;

[0077] If the preliminary judgment result is yes, that is, CHF phenomenon has occurred, then execute 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 a 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, 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 obtained for 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 all temperature change rates of the outer wall temperature of the same experimental body 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 misjudgment of the CHF phenomenon operating point and avoid missed judgment of the CHF phenomenon operating point, and automatically give 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 recognition system based on a support vector machine, which uses a CHF phenomenon recognition method based on a support vector machine in embodiment 1, and the system corresponds to the CHF phenomenon recognition method based on a support vector machine in embodiment 1 in a one-to-one manner. The system includes:

[0088] An acquisition unit, used to acquire the 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 of the measurement point data of the outer wall temperature of the experimental body, and obtain the temperature change rate data before and after the CHF occurs;

[0090] The model training unit is used to use the support vector machine algorithm to perform supervised learning on the temperature change rate data before and after the CHF occurs, and 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 data of the measuring points of the outer wall temperature of all experimental bodies after the temperature change rate conversion into the CHF intelligent judgment model based on the support vector machine in real time, perform preliminary CHF judgment, and obtain preliminary judgment results;

[0092] The second judgment unit is used for further CHF judgment based on the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point if the preliminary judgment result is a preliminary CHF phenomenon.

[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 surface of the experimental body, and the measuring 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 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 measuring point at the nth time step before, 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 to calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure 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. 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 The outlet temperature of the experimental body fluid is measured; 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 a CHF phenomenon identification method based on a 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 executable on the processor, wherein the processor implements the above-mentioned CHF phenomenon identification method based on a support vector machine when executing the computer program.

[0107] At the same time, the present invention also 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 above-mentioned CHF phenomenon identification method based on a support vector machine is implemented.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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; 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; A support vector machine algorithm is used to perform 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; Input the measured point data of the outer wall temperature of all experimental bodies into the CHF intelligent judgment model based on the support vector machine in real time after the data are converted into the temperature change rate, 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: 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; A second temperature measuring point is set on the outer wall surface of the experimental body, and the measuring point data of the outer wall temperature of the experimental body is obtained through the second temperature measuring point.

3. A CHF phenomenon identification method based on support vector machine according to claim 1, 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: 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; 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 measuring point at the nth time step before, 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 a 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, 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 obtained for 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 all temperature change rates of the outer wall temperature of the same experimental body 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 the 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, used to convert and classify the temperature change rate of the measurement point data of the outer wall temperature of the experimental body, and obtain the temperature change rate data before and after the CHF occurs; A model training unit, used for 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 a 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 data of the measuring points of the outer wall temperature of all experimental bodies after the temperature change rate conversion 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; The second judgment unit is used for further CHF judgment based on the test body fluid outlet temperature and the test body fluid outlet pressure to obtain the final CHF operating point if the preliminary judgment result is a preliminary CHF phenomenon.

8. A CHF phenomenon recognition system based on support vector machine according to claim 7, characterized in that: The second judging unit comprises: 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 to calculate the saturation temperature T corresponding to the experimental body fluid outlet pressure 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. 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 The outlet temperature of the experimental body fluid is measured; 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, characterized in that: When the processor executes the computer program, the CHF phenomenon identification method based on support vector machine as described in 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 as claimed in any one of claims 1 to 6 is implemented.

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