A method and system for assessing direct heating of containment vessel under severe nuclear power plant accidents.

Abstract

This disclosure provides a method and system for assessing direct containment heating under severe nuclear power plant accidents, relating to the field of containment risk assessment technology for pressurized water reactor nuclear power plants. The method includes: identifying key influencing factors of direct containment heating; acquiring typical severe accident sequences for batch simulation analysis; sampling key influencing factors based on the results of the batch simulation analysis to form a sample library; calculating the sample library samples by selecting conservative and optimized models to obtain the corresponding peak containment pressure under direct containment heating conditions, performing quantitative analysis; obtaining the probability distribution of containment pressure values ​​based on the quantitative analysis results; and determining the containment failure probability under the quantitative analysis of direct containment heating based on the probability distribution of containment pressure values ​​and the containment failure condition probability curve. This disclosure can obtain more reasonable and accurate containment failure probability analysis results.

Description

Technical Field

[0001] This disclosure relates to the field of containment risk assessment technology for pressurized water reactor nuclear power plants, specifically to a method and system for assessing direct heating of the containment under severe nuclear power plant accidents. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] In a severe accident at a pressurized water reactor nuclear power plant, if the reactor pressure vessel (RPV) lower head fails, the reactor coolant system (RCS) will be under high pressure. Molten core material, high-temperature steam, and residual coolant within the RPV will be ejected into the reactor cavity at high speeds. This ejected molten material and other substances may diffuse into other compartments of the containment vessel and heat the containment atmosphere through complex physicochemical processes, causing a rapid increase in pressure and temperature within the containment vessel. This could lead to premature containment failure; this process is called direct containment heating (DCH). Furthermore, unoxidized metals in the molten core will continue to react with oxygen within the containment vessel, releasing heat. The hydrogen produced by the oxidation reaction may also burn, further heating the containment atmosphere.

[0004] For the design of a nuclear power plant, conducting a DCH (Disaster-Collapse) accident process characteristic analysis and assessing whether the containment can remain intact when a DCH occurs helps to more comprehensively demonstrate the nuclear power plant's ability to cope with a DCH.

[0005] Existing DCH analyses primarily make a series of conservative assumptions about parameters such as initial conditions within the containment, the proportion of molten material injected into the reactor cavity after RPV failure, and the oxidation ratio of zirconium within the RPV. They employ deterministic methods to analyze the peak pressure within the containment after a DCH, thereby obtaining the most severe envelope-like consequences and providing input for containment structural integrity assessment. This method superimposes conservative values ​​from various influencing factors, resulting in highly conservative analysis results. While these results can serve as a reference for engineering design, they cannot represent actual accident conditions and cannot quantitatively analyze real power plant accident scenarios to obtain the probability of containment failure. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a method and system for assessing direct containment heating (DCH) under severe nuclear power plant accidents. It employs conservative model envelope analysis and optimized model precision calculations, introduces probabilistic methods, and samples and calculates key parameters influencing DCH analysis. From a probabilistic perspective, it analyzes the consequences of DCH, providing a more accurate and realistic quantitative analysis of the DCH phenomenon, thus obtaining the probability of containment failure.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] A method for assessing direct heating of the containment vessel under severe nuclear power plant accidents, comprising:

[0009] Based on the results and regularity conclusions of the deterministic analysis of the direct heating phenomenon of the containment, the key influencing factors of the direct heating phenomenon of the containment are screened; typical severe accident sequences are obtained for batch simulation analysis, a database is constructed, and the probability distribution of each key influencing factor is determined;

[0010] Based on the results of batch simulation analysis of severe accident sequences, key influencing factors are sampled to form a sample library, and input cards are generated for each sample.

[0011] By selecting conservative and optimized models to calculate the samples in the sample library, the peak containment pressure corresponding to the direct heating of the containment in a nuclear power plant is obtained, and quantitative analysis is performed. Based on the results of the quantitative analysis, the probability distribution of the containment pressure value is obtained.

[0012] Based on the probability distribution of containment pressure values ​​and the probability curve of containment failure conditions, the probability of containment failure under direct heating quantitative analysis is determined.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] A system for assessing direct heating of the containment vessel under severe nuclear power plant accidents includes:

[0015] The parameter acquisition module is used to screen key influencing factors of direct containment heating phenomenon based on the results and regularity conclusions of deterministic containment direct heating phenomenon analysis; acquire typical severe accident sequences for batch simulation analysis, build a database and determine the probability distribution of each key influencing factor;

[0016] The sample extraction module is used to sample key influencing factors based on the results of batch simulation analysis of severe accident sequences, form a sample library, and generate an input card for each sample.

[0017] The failure probability calculation module is used to calculate the peak containment pressure corresponding to the direct heating of the containment in a nuclear power plant by selecting conservative and optimized models. It performs quantitative analysis and obtains the probability distribution of containment pressure values ​​based on the results of the quantitative analysis.

[0018] Based on the probability distribution of containment pressure values ​​and the probability curve of containment failure conditions, the probability of containment failure under direct heating quantitative analysis is determined.

[0019] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0020] This disclosure presents a deterministic and probabilistic analytical method for the direct heating of containment (DCH) phenomenon under severe nuclear power plant accidents. (1) Based on a large number of accident sequence calculations and analysis, combined with engineering judgments, the probability distribution of key parameters affecting DCH analysis is determined; (2) DCH is quantitatively analyzed. The conservative model analysis results are relatively conservative and can be used for envelope analysis, while the optimized model can provide more accurate analysis results; (3) Important parameters are sampled and batch-processed, resulting in more reasonable and accurate containment failure probability analysis results compared to deterministic methods. This method compensates for the shortcomings of existing methods in DCH analysis and introduces sampling analysis techniques to sample and calculate factors that may affect the consequences of DCH, thereby obtaining a more reasonable and realistic containment failure probability. Detailed Implementation

[0021] The present disclosure will be further described below with reference to the embodiments.

[0022] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] Terminology Explanation:

[0025] DCH: Direct Containment Heating; RPV: Reactor Pressure Vessel; RCS: Reactor Coolant System; SG: Steam Generator.

[0026] Example 1

[0027] One embodiment of this disclosure provides a method for assessing direct heating of the containment vessel under severe nuclear power plant accidents, including:

[0028] Step 1: Based on the results and regularity conclusions of the deterministic analysis of the direct heating phenomenon of the containment, screen the key influencing factors of the direct heating phenomenon of the containment; obtain typical severe accident sequences for batch simulation analysis, construct a database, and determine the probability distribution of each key influencing factor;

[0029] Step 2: Based on the results of batch simulation analysis of severe accident sequences, sample the key influencing factors to form a sample library, and generate input cards for each sample;

[0030] Step 3: By selecting conservative and optimized models, the samples in the sample library are calculated to obtain the peak containment pressure corresponding to the direct heating of the containment in a nuclear power plant. Quantitative analysis is then performed, and based on the results of the quantitative analysis, the probability distribution of the containment pressure value is obtained.

[0031] Step 4: Based on the probability distribution of containment pressure values ​​and the probability curve of containment failure conditions, determine the containment failure probability under quantitative analysis of direct heating of the containment.

[0032] As one example, a method for assessing DCH failure under a severe nuclear power plant accident includes the following steps:

[0033] Step 1: Based on the results and regularity conclusions obtained from deterministic DCH analysis, and referring to existing DCH research findings, screen out the key influencing factors of the DCH phenomenon.

[0034] Step 2: Use a severe accident analysis program to conduct batch simulation analysis on a large number of typical severe accident sequences to form a total database. Based on the analysis results, combined with engineering judgment, determine the probability distribution of each key influencing factor in Step 1.

[0035] In step 1, the key initial influencing factors obtained are as follows:

[0036] (1) Containment conditions (initial containment pressure, initial containment temperature);

[0037] (2) Reactor coolant system (RCS) conditions (RCS volume, initial RCS fluid temperature);

[0038] (3) Molten material conditions (initial temperature of molten material, unoxidized fraction of core zirconium, proportion of molten material entering outside the reactor pressure vessel), etc.

[0039] In step 2, determining the probability distribution of each key influencing factor in step 1 includes: the probability distribution can be considered as the probability of the parameter within different ranges. Based on the results calculated by the severe accident analysis program, the main range of the parameter can be determined, and the probability within this range is considered a certain value, while the probabilities of being less than or greater than this range are also considered certain values. For example: when a DCH occurs, the containment pressure is mainly between 0.14 MPa(a) and 0.328 MPa(a). Therefore, the probability of the initial containment pressure being between 0.14 MPa(a) and 0.328 MPa(a) is considered relatively high, and is taken as 0.9. The probability of the initial containment pressure being between 0.101 MPa(a) and 0.140 MPa(a) and exceeding 0.328 MPa(a) is relatively low, and is taken as 0.05 respectively.

[0040] Step 3: Based on the overall results of the severe accident sequence analysis obtained in Step 2, a script is written to sample the selected key influencing factors, forming a sample library, and automatically generating input for each sample. The input card refers to standardizing the format of the samples (e.g., .txt files) to facilitate their use as input files in the DCH analysis program. Specifically, the sampling of the selected key influencing factors includes the following:

[0041] (1) Different random sampling methods can be used, including simple random sampling, systematic sampling, stratified sampling, etc. (e.g., Monte Carlo method, the characteristic of which is that the more sampling times, the closer the results are to the actual situation).

[0042] (2) Based on the given probability density function, samples are automatically drawn, and the number of samples drawn takes into account both accuracy and computational cost.

[0043] Different sample sizes affect the final containment failure probability, but when the sample size reaches a certain value, the error in the containment failure probability caused by the increase in the sample size is within an acceptable range (e.g., 0.5%), and the sample size can be considered to meet the given accuracy requirements; the computational cost is mainly the time cost consumed by the DCH analysis program; the sample size is automatically calculated by the DCH analysis program.

[0044] (3) Remove outliers that deviate significantly from the given probability density function.

[0045] When using sampling methods, it is necessary to construct a probability model (such as uniform distribution, normal distribution, etc.). The mathematical expression of this model is the corresponding probability density function, which is used to describe the probability of a random variable taking a value at a certain point.

[0046] Step 4: Write a script to call the DCH analysis program to perform batch processing calculations on the sampled data. By selecting a conservative model and an optimized model, batch process the sample library obtained in Step 3 to calculate the peak containment pressure corresponding to a DCH event in a nuclear power plant, thus achieving quantitative analysis. This includes the following:

[0047] (1) The conservative model assumes that the steam generator (SG) compartment and the upper space of the containment are a control volume unit, that the diffused melt is completely mixed throughout the containment, that heat exchange and chemical reactions have reached equilibrium, and that heat transfer to the structure is not considered.

[0048] (2) The optimization model assumes that the steam generator (SG) compartment and the upper space of the containment are two control volume units. During the calculation, the molten material ejected from the pressure vessel is distributed in different control volume units according to a certain proportion and calculated separately.

[0049] (3) Both conservative and optimized model DCH analyses mainly include the following processes and steps:

[0050] a) Calculate the RCS and the initial energy of each containment element (mainly for air and steam);

[0051] b) Calculate the energy released by DCH into each containment unit, including RCS release, core melt heating, Zr oxidation heat release within containment units, hydrogen combustion heat release outside the reactor core, and hydrogen combustion heat release inside the reactor core;

[0052] c) Calculate the heat capacity ratio between the molten material and the gas (including the original gas in the containment and the gas injected from the RCS into the containment) in each containment unit, including:

[0053] i. The physical meaning of the heat capacity ratio between the molten material and the gas within the containment unit is the energy ratio between the molten material and the gas after temperature equilibrium is reached. For a conservative model, the formula for calculating the heat capacity ratio between the molten material and the gas within the containment unit is:

[0054]

[0055] ii. For the optimization model, the formula for calculating the heat capacity ratio between the molten material and the gas in each containment unit is:

[0056]

[0057] In the formula: ψ is the heat capacity ratio of the molten material to the gas within the containment unit of the conservative model; ψ j To optimize the heat capacity ratio of molten material to gas within containment element j of the model; f aj The proportion of ejecta entering unit j to the total ejecta; f vj C represents the proportion of the volume of unit j to the total containment volume. UO2 Specific heat capacity of UO2; C Zr Specific heat capacity of Zr; N UO2 N represents the number of moles of UO2 injected into the containment unit. Zr N0 is the number of moles of Zr injected into the containment unit; N0 is the number of moles of air and steam initially present in the containment unit at the start of the DCH; NRCS is the number of moles of steam discharged into the containment unit by the RCS; C0 v This is the average specific heat capacity of air and steam within the containment unit.

[0058] Calculate the energy absorbed by the gas and vapor in each containment unit based on the heat capacity ratio:

[0059] i. For the conservative model, the formula for calculating the energy absorbed by the gas and vapor in the containment unit is:

[0060]

[0061] ii. For the optimized model, the formula for calculating the energy absorbed by gases and vapors within each containment unit is:

[0062]

[0063] In the formula: ΔU i The i-th type of energy absorbed by the gas within the containment unit; ΔU ij ψ represents the i-th type of energy absorbed by the gas within containment element j; ψ is the heat capacity ratio of the molten material to the gas within the containment element in the conservative model; ψ j To optimize the heat capacity ratio of molten material to gas within containment unit j of the model; Ei represents the i-th type of energy released into the containment in step b).

[0064] e) Calculate the pressure rise based on the total energy absorbed by the gases and vapors in each containment unit, and obtain the final pressure of the containment.

[0065] Step 5: Based on the quantitative analysis and results of Step 4 above, the probability distribution of containment pressure values ​​is obtained.

[0066] Step 5 involves obtaining the range of peak containment pressure based on the quantitative analysis results from Step 4. This pressure range is then uniformly divided into several intervals of a certain length, and the number of pressure results within each interval is determined to obtain the probability distribution curve of the containment pressure values.

[0067] Step 6: Based on the containment failure condition probability curve and the containment pressure probability distribution obtained in Step 5, the containment failure probability under the DCH quantitative analysis is finally determined. The calculation method for the containment failure condition probability is as follows:

[0068] (1) The log-normal distribution is used to construct the conditional probability distribution of containment failure. The log-normal distribution of the conditional probability of containment failure is calculated using the following distribution function:

[0069] F(x)~lognormal(μ,β 2 )

[0070] In the formula: F(x) represents the containment failure pressure; μ represents the log-normal expected value; β represents the log-normal standard deviation;

[0071] (2) Establishing the probability distribution of containment failure conditions requires confirming the median of containment failure modes. The optimal estimate of the containment yield pressure, X, can be used as the median of the containment failure pressure distribution. The optimal estimate of the containment yield pressure, X, can be obtained from the following relationship:

[0072] X = exp(μ)

[0073] Vt =[exp(β 2 )-1] 0.5

[0074] V t =(V 2 结构 +V 2 材料 ) 0.5

[0075] Where: μ represents the log-normal expected value; β represents the log-normal standard deviation; X represents the best estimate of the yield failure pressure of the containment shell or head; V t V represents the total coefficient of variation that takes into account all uncertainties; 结构 and V 材料 These represent the coefficients of variation corresponding to uncertainties in structural analysis and uncertainties in material properties, respectively.

[0076] Establishing the containment condition probability distribution requires detailing the median containment failure value for each possible containment failure mode. The median containment failure value refers to the median failure pressure in the containment failure condition probability distribution.

[0077] By combining the containment condition probability distribution curve with the containment pressure probability distribution obtained in step 5, the containment failure probability within different containment pressure distribution ranges under DCH quantitative analysis can be determined.

[0078] Example 2

[0079] One embodiment of this disclosure provides a direct containment heating assessment system under severe nuclear power plant accidents, comprising:

[0080] The parameter acquisition module is used to screen key influencing factors of direct containment heating phenomenon based on the results and regularity conclusions of deterministic containment direct heating phenomenon analysis; acquire typical severe accident sequences for batch simulation analysis, build a database and determine the probability distribution of each key influencing factor;

[0081] The sample extraction module is used to sample key influencing factors based on the results of batch simulation analysis of severe accident sequences, form a sample library, and generate an input card for each sample.

[0082] The failure probability calculation module is used to calculate the peak containment pressure corresponding to the direct heating of the containment in a nuclear power plant by selecting conservative and optimized models. It performs quantitative analysis and obtains the probability distribution of containment pressure values ​​based on the results of the quantitative analysis.

[0083] Based on the probability distribution of containment pressure values ​​and the probability curve of containment failure conditions, the probability of containment failure under direct heating quantitative analysis is determined.

[0084] While the specific embodiments of this disclosure have been described above, they are not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for assessing direct heating of the containment vessel under severe nuclear power plant accidents, characterized in that, include: Based on the results and regularity conclusions of the analysis of the direct heating phenomenon of containment, the key influencing factors of the direct heating phenomenon of containment are screened. Typical severe accident sequences were obtained for batch simulation analysis, a database was constructed, and the probability distribution of each key influencing factor was determined. Based on the results of batch simulation analysis of severe accident sequences, key influencing factors were sampled to form a sample library, and input cards were generated for each sample. By selecting conservative and optimized models to calculate the samples in the sample library, the peak containment pressure corresponding to the direct heating of the containment in a nuclear power plant is obtained, and quantitative analysis is performed. Based on the results of the quantitative analysis, the probability distribution of the containment pressure value is obtained. Based on the probability distribution of containment pressure values ​​and the containment failure condition probability curve, the containment failure probability under direct heating quantitative analysis is determined. The steps for quantitative analysis by selecting a conservative model and optimizing the model include: Calculate the initial energy of the reactor coolant system and each containment unit; calculate the energy released directly from the containment to each containment unit by heating, including reactor coolant system discharge, core molten material heating, zirconium oxidation heat release within the containment unit, hydrogen combustion heat release outside the reactor, and hydrogen combustion heat release inside the reactor; calculate the heat capacity ratio between the molten material and the gas within each containment unit; based on the heat capacity ratio, calculate the energy absorbed by the gas and steam within each containment unit; based on the total energy absorbed by the gas and steam within each containment unit, calculate the pressure rise and obtain the final pressure of the containment. Calculate the heat capacity ratio between the molten material and the gas within each containment unit. The gas includes both existing gases within the containment and gases injected into the containment from the reactor coolant system. The heat capacity ratio is calculated as follows: The physical meaning of the heat capacity ratio between the molten material and the gas within a containment unit is the energy ratio between the molten material and the gas after temperature equilibrium is reached. For a conservative model, the formula for calculating the heat capacity ratio between the molten material and the gas within a containment unit is: in, ψ The heat capacity ratio of the molten material to the gas within the containment unit of the conservative model; C UO2 for UO Specific heat capacity of 2; N UO2 For spraying into the containment unit UO The number of moles of 2; C Zr for Zr Specific heat capacity; N Zr For spraying into the containment unit Zr The number of moles; N 0 represents the number of moles of air and steam that were originally present in the containment unit at the initial stage of direct heating of the containment. N RCS This refers to the number of moles of steam discharged from the reactor coolant system into the containment unit. C v The average specific heat capacity of air and steam within the containment unit; For the optimization model, the formula for calculating the heat capacity ratio between the molten material and the gas in each containment unit is: in, ψ j To optimize the model containment unit j The heat capacity ratio of the molten material to the gas; f aj To enter the unit j The proportion of projectiles in the total projectiles; f vj For unit j The proportion of volume to the total containment volume; The process of calculating the energy absorbed by gases and vapors in each containment unit based on the heat capacity ratio includes: For a conservative model, the formula for calculating the energy absorbed by gases and vapors in a containment unit is: Where, ∆ U i The i-th type of energy absorbed by the gas within the containment unit; E i The first [unit] is released directly into the containment by heating. i A kind of energy; For the optimized model, the formulas for calculating the energy absorbed by gases and vapors within each containment unit are as follows: in, U ij For containment unit j The first internal gas absorption i A kind of energy.

2. The method for assessing direct heating of the containment vessel under severe nuclear power plant accident as described in claim 1, characterized in that, Key influencing factors for acquisition include: Initial containment pressure, initial containment temperature, reactor coolant system volume, initial temperature of reactor coolant system fluid, initial temperature of molten material, unoxidized core zirconium fraction, and proportion of molten material entering the reactor pressure vessel.

3. The method for assessing direct heating of the containment vessel under severe nuclear power plant accident as described in claim 1, characterized in that, Based on the results of batch simulation analysis of severe accident sequences, key influencing factors were sampled, including: Multiple random sampling methods are selected, and samples are automatically drawn using programming based on a given probability density function. The number of samples drawn takes into account both accuracy and computational cost, and an input card is generated for each sample. Outliers that deviate significantly from the given probability density function are removed.

4. The method for assessing direct heating of the containment vessel under severe nuclear power plant accident as described in claim 1, characterized in that, The method for calculating the conditional probability of containment failure is as follows: a log-normal distribution is used to construct the conditional probability distribution of containment failure, the median of containment failure modes is determined, and the best estimated value of containment yield pressure is used as the median of the containment failure pressure distribution.

5. A system for assessing direct heating of the containment vessel under severe nuclear power plant accidents, based on the method for assessing direct heating of the containment vessel under severe nuclear power plant accidents as described in any one of claims 1-4, characterized in that, include: The parameter acquisition module is used to screen key influencing factors of the direct heating phenomenon of the containment based on the results and regularity conclusions of the deterministic analysis of the direct heating phenomenon of the containment. Typical severe accident sequences were obtained for batch simulation analysis, a database was constructed, and the probability distribution of each key influencing factor was determined. The sample extraction module is used to sample key influencing factors based on the results of batch simulation analysis of severe accident sequences, form a sample library, and generate an input card for each sample. The failure probability calculation module is used to calculate the peak containment pressure corresponding to the direct heating of the containment in a nuclear power plant by selecting conservative and optimized models. It performs quantitative analysis and obtains the probability distribution of containment pressure values ​​based on the results of the quantitative analysis. Based on the probability distribution of containment pressure values ​​and the probability curve of containment failure conditions, the probability of containment failure under direct heating quantitative analysis is determined.

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