A nuclear power plant accident prediction system

Abstract

The application relates to a nuclear power plant accident prediction system, which comprises a data acquisition module, a prediction calculation module, a mitigation measure input module, a real-time evaluation module and a report output module; the data acquisition module is used for acquiring any transient operation data and model uncertainty parameters of the nuclear power plant and sending the data and the parameters to the prediction calculation module; the mitigation measure input module is used for classifying user-inputted mitigation measures and sending the classified mitigation measures to the prediction calculation module and the real-time evaluation module; the prediction calculation module is used for nuclear power plant accident prediction calculation, obtaining a prediction calculation result and sending the result to the implementation evaluation module and the report output module; the real-time evaluation module is used for real-time evaluation of the nuclear power plant accident, obtaining a real-time evaluation result and sending the result to the report output module; and the report output module is used for generating a time series prediction report. The nuclear power plant accident prediction system can quickly predict nuclear power plant accidents, develop nuclear accidents and give feasible accident mitigation measure suggestions.

Description

Technical Field

[0001] The present invention relates to the field of nuclear safety and nuclear emergency technology, and in particular to a method for quickly predicting pressurized water reactor nuclear accidents and evaluating mitigation measures. Background Art

[0002] Nuclear power plant accident emergency preparedness and response is the final line of defense for nuclear safety. Appropriate nuclear accident mitigation measures can effectively reduce accident risks, delay or halt the accident process, mitigate accident consequences, reduce or avoid potential radiation risks to the public, and protect the health and safety of the surrounding public. However, inappropriate nuclear accident emergency mitigation measures can also pose additional risks to public radiation safety. Summary of the Invention

[0003] Based on this, in order to solve the problem of rapid prediction of nuclear power plant accident emergency preparation and response, a nuclear power plant accident prediction system is provided. The nuclear power plant accident prediction system quickly predicts nuclear power plant accidents and nuclear power plant development nuclear accidents, and gives feasible accident mitigation measures to minimize the harm of nuclear accidents to the public.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A nuclear power plant accident prediction system includes a data acquisition module, a prediction and calculation module, a mitigation measure input module, a real-time evaluation module, and a report output module; the data acquisition module is used to collect arbitrary transient operating data and model uncertainty parameters of the nuclear power plant and send them to the prediction and calculation module; the mitigation measure insertion module is used for user input of mitigation measures, and the mitigation measures input by the user are classified and sent to the prediction and calculation module and the real-time evaluation module;

[0006] The prediction calculation module is used to receive any transient operation data and model uncertainty parameters of the nuclear power plant sent by the data acquisition module, as well as the mitigation measures sent by the mitigation measure insertion module; to perform prediction calculations on nuclear power plant accidents and obtain prediction calculation results; and to send the prediction calculation results to the implementation evaluation module and the report output module;

[0007] The real-time evaluation module is used to receive the mitigation measures sent by the mitigation measure insertion module and the prediction calculation results sent by the prediction calculation module; to evaluate the nuclear power plant accident in real time and obtain real-time evaluation results; and to send the real-time evaluation results to the report output module;

[0008] The report output module is used to receive the real-time evaluation results sent by the implementation evaluation module and the predictive calculation results sent by the predictive calculation module, analyze the impact of the main safety barrier status of the nuclear power plant, key accident events and mitigation measures based on the predictive calculation results, and generate a time series prediction report as a recommendation for the use of nuclear accident mitigation measures.

[0009] Furthermore, the prediction calculation module uses the internationally common severe accident analysis software MAAP5 as the calculation kernel, and builds a model based on the design characteristics of the pressurized water reactor nuclear power plant to perform MAAP5 instance calculations.

[0010] Furthermore, the prediction calculation module performs MAAP5 instance calculation, including the following steps: the prediction calculation module obtains transient operation data of the nuclear power plant and model uncertainty parameters, generates a prediction calculation initialization file, starts multiple MAAP instances to start prediction calculation, and multiple MAAP instances are cyclically calculated to obtain prediction calculation results.

[0011] Furthermore, the model uncertainty parameters include optimal estimation parameters, optimistic estimation parameters, suboptimal estimation parameters, conservative estimation parameters and over-conservative estimation parameters.

[0012] Furthermore, multiple MAAP instances are cyclically calculated, including the following steps:

[0013] During the calculation process of each MAAP instance, the prediction calculation module determines whether it has received an insert mitigation measure instruction; if the prediction calculation module has received an insert mitigation measure instruction, the MAAP instance corrects the calculation according to the insert mitigation measure instruction and obtains a prediction calculation result; if the prediction calculation module has not received an insert mitigation measure instruction, the MAAP instance performs calculation to obtain a prediction calculation result;

[0014] After obtaining the prediction calculation results, the prediction calculation module determines whether to stop the MAAP instance prediction calculation; if all MAAP instances have completed the prediction calculation, the MAAP instance prediction calculation is stopped; if there are still MAAP instances that have not completed the calculation, the MAAP instance continues to perform the prediction calculation until all MAAP instances have completed the calculation, and the MAAP instance prediction calculation is stopped.

[0015] Furthermore, the MAAP instance includes a best estimate instance, an optimistic estimate instance, a suboptimal estimate instance, a conservative estimate instance, and an over-conservative estimate instance.

[0016] Furthermore, the real-time evaluation module evaluates the prediction calculation results in real time to obtain the real-time evaluation results, including the following steps:

[0017] The real-time evaluation module receives the mitigation measures sent by the mitigation measure insertion module and the prediction calculation results sent by the prediction calculation module, and determines whether the mitigation measures are inserted in the prediction calculation results, whether the safety barrier fails, and whether the key event occurs;

[0018] If a mitigation measure is inserted into the prediction calculation result, the real-time evaluation module records the real-time evaluation result of the mitigation measure and sends it to the report output module;

[0019] If the safety barrier fails in the prediction calculation result, the real-time evaluation module records the safety barrier failure time and sends it to the report output module; if a key event occurs, the real-time evaluation module records the key event occurrence time and sends it to the report output module.

[0020] Furthermore, the mitigation measures module classifies mitigation measures according to general PWR nuclear power plant severe accident management guidelines, including but not limited to the following:

[0021] Table 1 Classification of mitigation measures

[0022]

[0023]

[0024] Furthermore, the real-time evaluation results of the mitigation measures include positive evaluations, negative evaluations, and long-term concerns of the mitigation measures.

[0025] In one embodiment, the mitigation measure is injecting water into the evaporator through a pump (SAG03). The real-time evaluation results of the mitigation measure include but are not limited to the following:

[0026] Table 2 Real-time evaluation results of SAG03 water source pumping water to evaporator

[0027]

[0028]

[0029] Furthermore, the real-time evaluation module is also used to display real-time evaluation results of safety barrier failure time, key event occurrence time and mitigation measures to the user.

[0030] Furthermore, the report output module is also used to display the time series prediction report to the user.

[0031] Furthermore, the safety barrier includes a cladding, fuel, a pressure vessel and a containment vessel.

[0032] Furthermore, the key events include but are not limited to the core outlet temperature reaching above 650° for the first time, reactor shutdown, and the evaporator water level dropping below 1 m for the first time.

[0033] Beneficial technical effects of the present invention:

[0034] The nuclear power plant accident prediction system of the present invention can use any transient operating data of the nuclear power plant and a set of model uncertainty parameters as the initial state of the prediction calculation; can simultaneously start five different MAAP calculation instances for parallel fast calculation; can insert mitigation measures during the prediction calculation process, thereby affecting the MAAP instance calculation logic; the calculation magnification of the fast prediction can reach more than 30 times the overall fast effect; can display the change curve of the main state parameters of the nuclear power plant; can identify and record the failure time of the main safety barrier of the nuclear power plant; can identify and record the occurrence time of key events of nuclear accidents; can identify and record the positive evaluation, negative evaluation and long-term focus of mitigation measures; can form a time-series prediction report based on the prediction calculation results, and provide feasible nuclear accident mitigation measures. Suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the structure of the nuclear power plant accident prediction system of the present invention;

[0036] Figure 2 This is a workflow diagram of the nuclear power plant accident prediction system of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0038] Example 1

[0039] See also Figure 1 and 2 The present invention provides a nuclear power plant accident prediction system, comprising a data acquisition module, a prediction and calculation module, a mitigation measure input module, a real-time evaluation module, and a report output module; the data acquisition module is used to collect any transient operation data and model uncertainty parameters of the nuclear power plant and send them to the prediction and calculation module; the mitigation measure insertion module is used for users to input mitigation measures, and the mitigation measures input by the users are classified and sent to the prediction and calculation module and the real-time evaluation module;

[0040] The prediction calculation module is used to receive any transient operation data and model uncertainty parameters of the nuclear power plant sent by the data acquisition module, as well as the mitigation measures sent by the mitigation measure insertion module; to perform prediction calculations on nuclear power plant accidents and obtain prediction calculation results; and to send the prediction calculation results to the implementation evaluation module and the report output module;

[0041] The real-time evaluation module is used to receive the mitigation measures sent by the mitigation measure insertion module and the prediction calculation results sent by the prediction calculation module; to evaluate the prediction calculation results in real time to obtain real-time evaluation results; and to send the real-time evaluation results to the report output module;

[0042] The report output module is used to receive the real-time evaluation results sent by the implementation evaluation module and the predictive calculation results sent by the predictive calculation module, analyze the impact of the main safety barrier status of the nuclear power plant, key accident events and mitigation measures based on the predictive calculation results, and generate a time series prediction report as a recommendation for the use of nuclear accident mitigation measures.

[0043] Furthermore, the model uncertainty parameters include optimal estimation parameters, optimistic estimation parameters, suboptimal estimation parameters, conservative estimation parameters and over-conservative estimation parameters.

[0044] Furthermore, the prediction calculation module uses the internationally common severe accident analysis software MAAP5 as the calculation kernel, and builds a model based on the design characteristics of the pressurized water reactor nuclear power plant to perform MAAP5 instance calculations.

[0045] Furthermore, the prediction calculation module performs MAAP5 instance calculation, including the following steps: the prediction calculation module obtains transient operation data of the nuclear power plant and model uncertainty parameters, generates a prediction calculation initialization file, starts multiple MAAP instances to start prediction calculation, and multiple MAAP instances are cyclically calculated to obtain prediction calculation results.

[0046] Furthermore, multiple MAAP instances are cyclically calculated, including the following steps:

[0047] During the calculation process of each MAAP instance, the prediction calculation module determines whether it has received an insert mitigation measure instruction; if the prediction calculation module has received an insert mitigation measure instruction, the MAAP instance corrects the calculation according to the insert mitigation measure instruction and obtains a prediction calculation result; if the prediction calculation module has not received an insert mitigation measure instruction, the MAAP instance performs calculation to obtain a prediction calculation result;

[0048] After obtaining the prediction calculation results, the prediction calculation module determines whether to stop the MAAP instance prediction calculation; if all MAAP instances have completed the prediction calculation, the MAAP instance prediction calculation is stopped; if there are still MAAP instances that have not completed the calculation, the MAAP instance continues to perform the prediction calculation until all MAAP instances have completed the calculation, and the MAAP instance prediction calculation is stopped.

[0049] Furthermore, the MAAP instance includes a best estimate instance, an optimistic estimate instance, a suboptimal estimate instance, a conservative estimate instance, and an over-conservative estimate instance.

[0050] Furthermore, the mitigation measure insertion module classifies mitigation measures according to general PWR nuclear power plant severe accident management guidelines, including but not limited to the following:

[0051] Table 1 Classification of mitigation measures

[0052]

[0053]

[0054] Severe Accident Management Guidelines for Pressurized Water Reactor Nuclear Power Plants are referred to as SAMG.

[0055] Furthermore, the real-time evaluation module evaluates the nuclear power plant accident prediction calculation results in real time to obtain the real-time evaluation results, including the following steps:

[0056] The real-time evaluation module receives the mitigation measures sent by the mitigation measure insertion module and the prediction calculation results sent by the prediction calculation module, and determines whether the mitigation measures are inserted in the prediction calculation results, whether the safety barrier fails, and whether the key event occurs;

[0057] If mitigation measures are inserted into the prediction calculation results, the real-time evaluation module records the real-time evaluation results of the mitigation measures and sends them to the report output module; if the safety barrier fails, the real-time evaluation module records the safety barrier failure time and sends it to the report output module; if a critical event occurs, the real-time evaluation module records the time of occurrence of the critical event and sends it to the report output module.

[0058] Furthermore, the real-time evaluation module is also used to display real-time evaluation results of safety barrier failure time, key event occurrence time and mitigation measures to the user.

[0059] Furthermore, the real-time evaluation results of the mitigation measures include positive evaluations, negative evaluations, and long-term focus item change times of the mitigation measures.

[0060] For example, if the mitigation measure SAG03 is inserted into the prediction calculation results: injecting water into the evaporator through a pump from a water source, the typical real-time evaluation results of the mitigation measure are as follows:

[0061] Table 2 Real-time evaluation results of SAG03 water source pumping water to evaporator

[0062]

[0063] Furthermore, the safety barrier includes a cladding, fuel, a pressure vessel and a containment vessel.

[0064] Furthermore, the key events include but are not limited to the core outlet temperature reaching above 650° for the first time, reactor shutdown, and the evaporator water level dropping below 1 m for the first time.

[0065] Furthermore, the report output module is also used to display the time series prediction report to the user.

[0066] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A nuclear power plant accident prediction system, characterized in that: It includes a data acquisition module, a prediction and calculation module, a mitigation measure input module, a real-time evaluation module and a report output module; the data acquisition module is used to collect any transient operation data and model uncertainty parameters of the nuclear power plant and send them to the prediction and calculation module; the mitigation measure insertion module is used for users to input mitigation measures, and classifies the mitigation measures input by users and sends them to the prediction and calculation module and the real-time evaluation module; The prediction calculation module is used to receive arbitrary transient operation data and model uncertainty parameters of the nuclear power plant sent by the data acquisition module, and the mitigation measures sent by the mitigation measure insertion module; Used for predicting and calculating nuclear power plant accidents to obtain prediction and calculation results; and used for sending the prediction and calculation results to the implementation evaluation module and the report output module; The real-time evaluation module is used to receive the mitigation measures sent by the mitigation measure insertion module and the prediction calculation results sent by the prediction calculation module; Used for real-time evaluation of nuclear power plant accidents and obtaining real-time evaluation results; and used for sending the real-time evaluation results to the report output module; The report output module is used to receive the real-time evaluation results sent by the implementation evaluation module and the prediction calculation results sent by the prediction calculation module, analyze the impact of the main safety barrier status of the nuclear power plant, key accident events and mitigation measures based on the prediction calculation results, and generate a time series prediction report; The model uncertainty parameters include best estimation parameters, optimistic estimation parameters, suboptimal estimation parameters, conservative estimation parameters and over-conservative estimation parameters; the mitigation measures module classifies mitigation measures according to the general pressurized water reactor nuclear power plant severe accident management guidelines; the real-time evaluation results of the mitigation measures include positive evaluations, negative evaluations and long-term concerns of the mitigation measures, and the specific evaluation methods and contents refer to the general pressurized water reactor nuclear power plant severe accident management guidelines.

2. The nuclear power plant accident prediction system according to claim 1, characterized in that: The prediction calculation module uses the internationally common severe accident analysis software MAAP5 as the calculation core, builds a model based on the design characteristics of pressurized water reactor nuclear power plants, and performs MAAP5 instance calculations.

3. The nuclear power plant accident prediction system according to claim 2, characterized in that: The prediction calculation module performs MAAP5 instance calculation, including the following steps: the prediction calculation module obtains transient operation data of the nuclear power plant and model uncertainty parameters, generates a prediction calculation initialization file, starts multiple MAAP instances to start prediction calculation, and the multiple MAAP instances are cyclically calculated to obtain prediction calculation results.

4. The nuclear power plant accident prediction system according to claim 3, characterized in that: Multiple MAAP instances are calculated cyclically, including the following steps: During the calculation process of each MAAP instance, the prediction calculation module determines whether it has received an insert mitigation measure instruction; if the prediction calculation module has received an insert mitigation measure instruction, the MAAP instance corrects the calculation according to the insert mitigation measure instruction and obtains a prediction calculation result; if the prediction calculation module has not received an insert mitigation measure instruction, the MAAP instance performs calculation to obtain a prediction calculation result; After obtaining the prediction calculation result, the prediction calculation module determines whether to stop the MAAP instance prediction calculation; If all MAAP instances have completed the prediction calculation, stop the MAAP instance prediction calculation; If there are still MAAP instances that have not completed the calculation, the MAAP instance continues to perform the prediction calculation until all MAAP instances have completed the calculation, and then the MAAP instance prediction calculation is stopped.

5. The nuclear power plant accident prediction system according to claim 4, characterized in that: The multiple MAAP instances include a best estimate instance, an optimistic estimate instance, a suboptimal estimate instance, a conservative estimate instance, and an over-conservative estimate instance.

6. The nuclear power plant accident prediction system according to claim 1, characterized in that: The real-time evaluation module evaluates the prediction calculation results in real time to obtain the real-time evaluation results, including the following steps: The real-time evaluation module receives the mitigation measures sent by the mitigation measure insertion module and the prediction calculation results sent by the prediction calculation module, and determines whether the mitigation measures are inserted in the prediction calculation results, whether the safety barrier fails, and whether the key event occurs; If a mitigation measure is inserted into the prediction calculation result, the real-time evaluation module records the real-time evaluation result of the mitigation measure and sends it to the report output module; If the safety barrier fails in the prediction calculation result, the real-time evaluation module records the safety barrier failure time and sends it to the report output module; If a key event occurs, the real-time evaluation module records the time of occurrence of the key event and sends it to the report output module.

7. The nuclear power plant accident prediction system according to claim 6, characterized in that: The safety barrier includes a cladding, fuel, a pressure vessel and a containment vessel.

8. The nuclear power plant accident prediction system according to claim 6, characterized in that: The key events include but are not limited to the core outlet temperature reaching above 650° for the first time, reactor shutdown, and the evaporator water level dropping below 1 m for the first time.

9. The nuclear power plant accident prediction system according to any one of claims 1 to 8, characterized in that: The real-time evaluation module is also used to display real-time evaluation results of safety barrier failure time, key event occurrence time and mitigation measures to users.

10. The nuclear power plant accident prediction system according to any one of claims 1 to 8, characterized in that: The report output module is also used to display the time series prediction report to the user.

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