Quantitative analysis method for operation performance of fluid system of nuclear power plant

Abstract

The invention belongs to the technical field of system simulation analysis, and particularly relates to a quantitative analysis method for operation performance of a fluid system of a nuclear power plant. The method comprises the following steps: S1, selecting a to-be-analyzed fluid system, and defining a system boundary; then selecting a target performance parameter according to the function requirement of the system, if the target performance parameter has a strongly correlated unit measurement point, taking the measurement point as a target measurement point, and entering the step S2, otherwise, taking the target performance parameter as the target measurement point, and entering the step S3; s2, establishing an operation database, quantitatively evaluating the correlation between each measuring point in the operation data and a target measuring point, and then taking the measuring point with high correlation with the target measuring point as an influence factor according to the strength of the correlation; and S3, selecting important influence factors of the target measurement point by adopting an expert judgment mode. Quantitative analysis and evaluation of the operation performance of the fluid system of the nuclear power plant are realized, key factors restricting the operation performance of the system are mined, and the operation state of the system is assisted to be optimized.

Description

Technical Field

[0001] The invention belongs to the technical field of system simulation analysis, and in particular relates to a quantitative analysis method for the operating performance of a fluid system in a nuclear power plant. Background Art

[0002] The fluid system is an important component of the nuclear power plant system and an important carrier for realizing the thermal cycle of the nuclear power plant. The huge amount of heat released by the chain fission reaction in the core is carried out by the high-temperature and high-pressure coolant in the first loop. When the coolant flows through the steam generator, the heat is transferred to the second loop system through the U-shaped heat transfer tube. The second loop fluid system then performs the Rankine thermodynamic cycle to convert the thermal energy into mechanical energy and electrical energy. Therefore, the operating performance of the fluid system is of great significance to the efficient and safe operation of the nuclear power plant. However, during the actual operation of the nuclear power plant, the number of operating conditions is large and the process is complex. It is inevitable that the state will exceed the design expectations. Moreover, with the aging and failure of the equipment, the overall performance of the fluid system will also change. Therefore, how to quantitatively analyze and evaluate the operating performance of the power plant fluid system is an important research topic. Summary of the invention

[0003] The purpose of the present invention is to provide a quantitative analysis method for the operating performance of a nuclear power plant fluid system, to achieve quantitative analysis and evaluation of the operating performance of a nuclear power plant fluid system, to identify key factors that restrict the operating performance of the system, and to assist in optimizing the operating status of the system.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A method for quantitatively analyzing the operating performance of a fluid system of a nuclear power plant comprises the following steps:

[0006] S1. The nuclear power plant has divided the fluid system according to its function during design. Therefore, it is necessary to first select the fluid system to be analyzed and clarify the system boundary. Then, the target performance parameter is selected according to the functional requirements of the system. If the target performance parameter has a strongly associated unit measurement point, this measurement point is used as the target measurement point and the process proceeds to step S2. Otherwise, the target performance parameter is regarded as the target measurement point and the process proceeds to step S3.

[0007] S2. Establish an operation database and quantitatively evaluate the correlation between each measurement point in the operation data and the target measurement point. Then, based on the strength of the correlation, the measurement point with a high correlation with the target measurement point is used as an influencing factor;

[0008] S3. Use expert judgment to select important influencing factors of the target measurement points;

[0009] S4. Based on the theories of fluid mechanics and thermodynamics, establish a simulation model of the fluid system and use operating data to verify the steady-state and transient accuracy of the model;

[0010] S5. Use uncertainty analysis methods to quantitatively calculate the correlation and sensitivity between the target measuring point and each influencing factor, and give the confidence level of the calculation results; then draw the response surface curves of each influencing factor and the target measuring point based on the uncertainty analysis sample data;

[0011] S6. Based on the analysis results of S5, determine the core influencing factors of the target parameters; determine the ideal range of the target measurement points according to the system functional requirements, and optimize the system performance based on the response surface curve.

[0012] S2. Quantitative evaluation of correlation was performed by calculating Pearson and Spearman correlation coefficients.

[0013] S2. The operation database should contain all the measurement point data of the target system under various operating conditions, and use the basic conservation principle and redundant measurement point processing principle to eliminate abnormal data.

[0014] Basic conservation principles refer to the conservation principles of mass, energy and momentum.

[0015] The total flow rate of each branch pipeline should be equal to the main pipe flow rate. When there is a difference, the high-precision measuring point result should be used to correct the low-precision measuring point result.

[0016] The principle of redundant measuring point processing is that when the errors of the three redundant measuring points are both less than 5%, the average is taken.

[0017] S3. The final important influencing factor parameter set is the union of the influencing factors in steps S2 and S3.

[0018] S5. The output parameter of the uncertainty analysis is the target measurement point, and the input parameter is the union of the influencing factors selected in steps S2 and S3.

[0019] S5. Uncertainty analysis methods refer to input parameter uncertainty propagation methods.

[0020] S5. Uncertainty analysis methods include Monte Carlo method, deterministic sampling method and polynomial chaos method.

[0021] The beneficial effects achieved by the present invention are:

[0022] This method uses a combination of unit data correlation calculation and expert judgment to screen out the influencing factors of system performance, which is both accurate and comprehensive. Through quantitative analysis of unit operation data, even if the system operation status deviates from the expected design or the equipment ages and fails, it can accurately reflect the actual operation characteristics of the system. Moreover, on this basis, expert theory and experience judgment are combined to ensure the comprehensiveness of the selection of influencing factors.

[0023] Secondly, the operating data under steady-state and transient conditions are used to verify the simulation model to ensure that the model can accurately reflect the actual physical process, thus providing a guarantee for the reliability and credibility of subsequent quantitative analysis work.

[0024] Finally, the uncertainty analysis method is used to quantitatively calculate the correlation and sensitivity values, and give the confidence level of the analysis results. At the same time, the response surface curve can be drawn based on the large number of samples generated during the analysis process, so that the characteristics of the system can be quantitatively analyzed to the greatest extent and reliably. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The figure is a flow chart of a quantitative analysis method for the operating performance of a fluid system in a nuclear power plant;

[0026] Figure 2 A simplified diagram of the fluid system. DETAILED DESCRIPTION

[0027] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1 As shown, a method for quantitatively analyzing the operating performance of a nuclear power plant fluid system includes the following steps:

[0029] S1. Since the fluid system of the nuclear power plant has been divided according to function during design, it is necessary to first select the fluid system to be analyzed and clarify the system boundary. Then select the target performance parameter according to the functional requirements of the system. The number of target performance parameters can be more than one. If the target performance parameter has a strongly associated unit measurement point, this measurement point is used as the target measurement point and enters step S2. Otherwise, the target performance parameter is regarded as the target measurement point and enters step S3.

[0030] S2. Establish an operation database and quantitatively evaluate the correlation between each measurement point in the operation data and the target measurement point. Then, based on the strength of the correlation, the measurement point with a high correlation with the target measurement point is used as an influencing factor.

[0031] The quantitative evaluation of the correlation can be completed by calculating the Pearson and Spearman correlation coefficients and other methods. The operation database should contain all the measurement point data of the target system under various operating conditions, and use the basic conservation principles and redundant measurement point processing principles to eliminate abnormal data. The basic conservation principles mainly refer to the principles of conservation of mass, energy and momentum. For example, the sum of the branch flow rates of the branch pipeline should be equal to the main pipe flow rate. When there is a difference, the high-precision measurement point results should be used to correct the low-precision measurement point results. The redundant measurement point principle, for example, for three redundant measurement points, when the errors between the two are less than 5%, the average is taken.

[0032] S3. Using expert judgment, select the important influencing factors of the target measuring point based on theory and experience. The final influencing factor parameter set is the union of the influencing factors in steps S2 and S3.

[0033] S4. Based on the theories of fluid mechanics and thermodynamics, a simulation model of the fluid system is established, and the steady-state and transient accuracy of the model is verified using operating data.

[0034] S5. Use uncertainty analysis methods to quantitatively calculate the correlation and sensitivity between the target measurement point and each influencing factor, and give the confidence level of the calculation results. Then, based on the uncertainty analysis sample data, draw the response surface curves of each influencing factor and the target measurement point.

[0035] The output parameter of the uncertainty analysis is the target measurement point, and the input parameter is the union of the influencing factors selected in steps S2 and S3. The uncertainty analysis method refers to the input parameter uncertainty propagation method, which mainly includes the Monte Carlo method, the deterministic sampling method and the polynomial chaos method.

[0036] S6. Based on the analysis results of S5, determine the core influencing factors of the target parameters. Determine the ideal range of the target measurement points according to the system functional requirements, and optimize the system performance based on the response surface curve.

[0037] Below Figure 2 Taking the fluid system shown in FIG. 1 as an example, the specific implementation process of the present invention is described as follows:

[0038] S1. The system controls valve V1 to mix cold and hot fluids in proportion, thereby providing constant temperature fluid to pump P1. According to the above system functional requirements, the temperature of the fluid sucked into the pump is used as the target performance parameter. Since there is a temperature measurement point at the outlet of valve V1, this measurement point is strongly related to the target performance parameter.

[0039] Therefore, this measuring point is used as the target measuring point.

[0040] S2. Collect system operation data, including start-stop conditions and steady-state operation conditions at each power step. Use the redundant measurement point processing principle to merge the redundant measurement point values ​​into one, and then calculate the Spearman correlation coefficient value between the target measurement point and other measurement points. Select the measurement point parameters with greater correlation as the influencing factors.

[0041] S3. By using the expert judgment method, based on theory and experience, it is believed that the flow parameters at flow sensor 3 can be further decomposed into equivalent TK2 liquid level, pump P2 head and valve V2 opening, and the liquid level of tank TK1 is also an important influencing factor. Therefore, the flow at flow sensor 3 selected in step S2 is replaced, and the liquid level of tank TK1 is supplemented to finally form a set of influencing factors.

[0042] S4. Based on the theories of fluid mechanics and thermodynamics, a simulation model of the fluid system is established, and the steady-state and transient accuracy of the model is verified using operating data.

[0043] S5. The uncertainty propagation method of input parameters based on the deterministic sampling method is used to quantitatively calculate the correlation and sensitivity between the target measuring point and each influencing factor, and the confidence level of the calculation result is given. Then, based on the uncertainty analysis sample data, the response surface curves of each influencing factor and the target measuring point are drawn.

[0044] S6. Based on the analysis results of S5, determine the core influencing factors of the target parameters. Determine the ideal range of the target measurement points according to the system functional requirements, and optimize the system performance based on the response surface curve.

Claims

1. A quantitative analysis method for the operating performance of a nuclear power plant fluid system, characterized in that: The following steps are involved: S1. The nuclear power plant has divided the fluid system according to its function during design. Therefore, it is necessary to first select the fluid system to be analyzed and clarify the system boundary. Then, the target performance parameter is selected according to the functional requirements of the system. If the target performance parameter has a strongly associated unit measurement point, this measurement point is used as the target measurement point and the process proceeds to step S2. Otherwise, the target performance parameter is regarded as the target measurement point and the process proceeds to step S3. S2. Establish an operation database and quantitatively evaluate the correlation between each measurement point in the operation data and the target measurement point. Then, based on the strength of the correlation, the measurement point with a high correlation with the target measurement point is used as an influencing factor; S3. Use expert judgment to select important influencing factors of the target measurement points; S4. Based on the theories of fluid mechanics and thermodynamics, establish a simulation model of the fluid system and use operational data to verify the steady-state and transient accuracy of the model; S5. Use uncertainty analysis methods to quantitatively calculate the correlation and sensitivity between the target measuring point and each influencing factor, and give the confidence level of the calculation results; then draw the response surface curves of each influencing factor and the target measuring point based on the uncertainty analysis sample data; S6. Based on the analysis results of S5, determine the core influencing factors of the target parameters; determine the ideal range of the target measurement points according to the system functional requirements, and optimize the system performance based on the response surface curve.

2. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 1, characterized in that: S2. Quantitative evaluation of correlation was performed by calculating Pearson and Spearman correlation coefficients.

3. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 1, characterized in that: S2. The operation database should contain all the measurement point data of the target system under various operating conditions, and use the basic conservation principle and redundant measurement point processing principle to eliminate abnormal data.

4. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 3, characterized in that: Basic conservation principles refer to the conservation principles of mass, energy and momentum.

5. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 4, characterized in that: The total flow rate of each branch pipeline should be equal to the main pipe flow rate. When there is a difference, the high-precision measuring point result should be used to correct the low-precision measuring point result.

6. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 3, characterized in that: The principle of redundant measuring point processing is that when the errors of the three redundant measuring points are both less than 5%, the average is taken.

7. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 1, characterized in that: S3. The final important influencing factor parameter set is the union of the influencing factors in steps S2 and S3.

8. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 1, characterized in that: S5. The output parameter of the uncertainty analysis is the target measurement point, and the input parameter is the union of the influencing factors selected in steps S2 and S3.

9. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 1, characterized in that: S5. Uncertainty analysis methods refer to input parameter uncertainty propagation methods.

10. The method for quantitative analysis of the operating performance of a nuclear power plant fluid system according to claim 9, characterized in that: S5. Uncertainty analysis methods include Monte Carlo method, deterministic sampling method and polynomial chaos method.

Images