CN113094976B - Data assimilation method and system for steam generator of pressurized water reactor nuclear power plant - Google Patents
Data assimilation method and system for steam generator of pressurized water reactor nuclear power plant Download PDFInfo
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Abstract
The invention discloses a method and a system for data assimilation of a steam generator of a pressurized water reactor nuclear power plant, which are used for analyzing the parameter sensitivity of a simulation model of the steam generator of the nuclear power plant; simulating a data acquisition process of a real nuclear power plant steam generator in an actual operation process or directly acquiring an actual observation value of a nuclear power plant; arranging and evaluating parameters of a simulation model of the steam generator of the nuclear power plant according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the simulation model of the steam generator of the nuclear power plant through a polynomial to be used as an observation operator; establishing an uncertainty database; and constructing a cost function by utilizing the uncertainty database and the actual observation value, calculating the cost function by using a genetic algorithm to obtain optimized parameters to be assimilated, bringing the optimized parameters into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence precision. The present invention facilitates maintenance and operation of the steam generator.
Description
Technical Field
The invention belongs to the technical field of reactor engineering, and particularly relates to a method and a system for data assimilation of a steam generator of a pressurized water reactor nuclear power plant.
Background
The development of nuclear reactor technology provides a reliable solution for human beings to obtain clean and efficient energy. In a pressurized water reactor nuclear power plant, a steam generator is an energy conversion device connecting a primary circuit and a secondary circuit, and the safe operation of the steam generator is crucial to the nuclear power plant. The steam generator simulation model is an important method for understanding the change of the thermodynamic and hydraulic dynamic characteristics of the steam generator, and has been widely applied to the research on the characteristics of the steam generator and the water level adjustment.
The uncertainty existing in the steam generator simulation model can be mainly summarized as follows: the existing steam generator simulation model structure cannot truly reflect the thermal hydraulic process of the steam generator in actual operation; the deterministic relationship among the internal phenomena of the steam generator is complex, and the model adopts a large number of mathematical physical equations to approximately simulate the relationship; the steam generator simulation model does not consider the influence of the performance change of the equipment caused by heat transfer pipe scale and the like on the steam generator in the actual operation process of the steam generator.
Model parameters of a simulation model of a steam generator of a pressurized water reactor nuclear power plant are usually adjusted manually. The manual method has a certain subjectivity and is related to the expert experience and the understanding degree of the model structure of the steam generator. Meanwhile, the steam generator simulation model has the characteristics of complex and various parameters, strong coupling and nonlinearity. Therefore, manually adjusting the model parameters according to expert experience often fails to actually change the model parameters in the steam generator simulation model.
Data assimilation is a mathematical method that reasonably and efficiently fuses together a variety of different data information to obtain the most accurate estimate of state. Data assimilation is used as a link for connecting a coefficient value model and observation data, and not only can initial parameters and initial conditions of a numerical model be determined, but also state parameters and model parameters of the model can be estimated and optimized.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method and a system for assimilating data of a steam generator of a pressurized water reactor nuclear power plant, aiming at the defects in the prior art, wherein the sensitivity and the uncertainty of model parameters are determined by using a sensitivity analysis method and an uncertainty analysis method, and the adjustment, optimization and estimation of the model parameters are realized by using the data assimilation method, so that the precision and the estimation effect of a simulation model are greatly improved.
The invention adopts the following technical scheme:
a data assimilation method for a steam generator of a pressurized water reactor nuclear power plant comprises the following steps:
s1, analyzing the sensitivity of parameters of a steam generator simulation model of a nuclear power plant to obtain a first-order sensitivity index and a global sensitivity index of the parameters of the steam generator simulation model of the nuclear power plant to the water level of the steam generator simulation model;
s2, simulating a data acquisition process of a real nuclear power plant steam generator in an actual operation process or directly acquiring an actual observation value of the nuclear power plant;
s3, arranging and evaluating parameters of the nuclear power plant steam generator simulation model in the step S1 according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the nuclear power plant steam generator simulation model through a polynomial to be used as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database;
and S4, constructing a cost function by using the uncertainty database established in the step S3 and the actual observation value obtained in the step S2, calculating the cost function by using a genetic algorithm to obtain an optimized parameter to be assimilated, bringing the optimized parameter into a steam generator simulation model, and assimilating the data after the output water level of the steam generator simulation model reaches convergence accuracy.
Specifically, step S1 specifically includes:
performing multi-group combined sampling on model parameters of a simulation model of the steam generator of the nuclear power plant based on the actual operation process of the steam generator by adopting a global sensitivity analysis (SOBOL) method; taking the sampling parameters as the model input of the steam generator simulation model, performing multiple times of steam generator simulation model operation, and storing the calculation output data of the steam generator simulation model; and processing the output data of the steam generator simulation model to obtain a first-order sensitivity index and a global sensitivity index of each parameter to the water level.
Further, the model parameters select heat exchange or resistance parameters of each node of the steam generator simulation model.
Specifically, in step S3, an assimilation model parameter is selected; and adopting a Mote-Carlo method to carry out combined sampling on the assimilation model parameters, taking the assimilation model parameters as the model input of the steam generator simulation model, carrying out multiple times of steam generator simulation model operation, and collecting the output data of the steam generator simulation model.
Further, the model parameters in the step S1 are sequenced according to the sensitivity indexes, and the heat exchange and resistance parameters with the maximum sensitivity indexes are determined as the model parameters to be assimilated.
Specifically, in step S4, a model parameter set value of the steam generator simulation model is used as a background value item in the cost function; constructing a background error covariance matrix item of the cost function by using the uncertainty of the parameter to be assimilated in the step S3; constructing an observation error covariance matrix item of the cost function by using the observation data in the step S2; taking the actual observation value in the step S2 as an observation value item of the cost function; and using the observation operator in the step S3 as an observation operator item of the cost function.
Specifically, in step S4, a global optimization algorithm genetic algorithm is adopted to solve an optimal model parameter solution which enables the cost function to obtain a global minimum, online adjustment is performed on model parameters of the steam generator simulation model, a deterministic coefficient of the steam generator simulation model is constructed, and the simulation accuracy of the normalized model is evaluated.
Another technical solution of the present invention is a pressurized water reactor nuclear power plant steam generator data assimilation system, comprising:
the analysis module is used for analyzing the sensitivity of the parameters of the steam generator simulation model of the nuclear power plant to obtain a first-order sensitivity index and a global sensitivity index of the parameters of the steam generator simulation model of the nuclear power plant to the water level of the steam generator simulation model;
the simulation module simulates the data acquisition process of a real nuclear power plant steam generator in the actual operation process or directly acquires the actual observation value of the nuclear power plant;
the database module is used for arranging and evaluating parameters of the nuclear power plant steam generator simulation model of the analysis module according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the nuclear power plant steam generator simulation model through a polynomial to serve as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database;
and the assimilation module is used for constructing a cost function by utilizing an uncertainty database established by the database module and an actual observation value obtained by the simulation module, calculating the cost function by using a genetic algorithm to obtain an optimized parameter to be assimilated, bringing the optimized parameter into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence precision.
Compared with the prior art, the invention at least has the following beneficial effects:
the invention discloses a data assimilation method for a steam generator of a pressurized water reactor nuclear power plant. Model parameters of a simulation model of a steam generator of a nuclear power plant are usually adjusted by a manual method, and the simulation model of the steam generator is difficult to realize due to the fact that the parameters of the simulation model of the steam generator are complex and various and have the characteristics of strong coupling and nonlinearity. In the operation process of the pressurized water reactor nuclear power plant, the steam generator can generate a large amount of observation data, the observation data is effectively utilized to realize the adjustment of model parameters, a model which accurately reflects the operation characteristics of the actual steam generator is obtained, meanwhile, the estimation of internal structure parameters and the water level of the steam generator can be realized by effectively estimating the model parameters, and the operation and the maintenance of the steam generator are facilitated.
Further, the actual operation of the steam generator of the nuclear power plant is a flow heat exchange process. The model parameters of the steam generation simulation model comprise a resistance coefficient and a heat exchange coefficient. The change of the model parameters can affect the dynamic characteristics of the model, and meanwhile, the model parameters have uncertainty, so that the value range of each model parameter is reasonably set according to observation data. Different model parameter combinations result in different model dynamic characteristic reflections, so that the model parameters are subjected to multi-group combination sampling. And taking different model parameter combinations as steam generator simulation model input, performing multiple model operations, and storing model output data. And processing the output data of the model to obtain a first-order sensitivity index and a global sensitivity index of each parameter to the water level.
Furthermore, the steam generation simulation model comprises structural parameters and model parameters, wherein the structural parameters are invariable, and the model parameters comprise a resistance coefficient and a heat exchange coefficient. And selecting the resistance or heat exchange parameters of the model nodes according to the flow heat exchange characteristics of the steam generator of the nuclear power plant.
Further, different model parameters have different degrees of influence on the dynamic characteristics of the model. And sequencing the steam generator simulation models according to the sensitivity indexes, and selecting sensitive heat exchange parameters and resistance parameters as parameters to be assimilated. And carrying out multi-group combined sampling on the assimilation model parameters by using a Mote-Carlo method, taking the assimilation model combined parameters as the input of the steam generator simulation model, carrying out multiple times of steam generator simulation model operation, and collecting output data of the steam generator simulation model. And calculating the uncertainty of each parameter to be assimilated and the combination of the parameters to be assimilated based on the variance theory. And fitting the mapping relation between the parameter to be assimilated and the model by a polynomial as the relation between the parameter to be assimilated and the actual observed data (observation operator).
Further, step S3 attributes the dynamic behavior of the steam generator model to the real process due to uncertainty of the sensitivity parameters and ignores the influence of the insensitivity parameters on the model output. And quantifying the influence degree of the parameter to be assimilated on the model output through the uncertainty.
Furthermore, a cost function is constructed, and the model parameters are assimilated. And the background error covariance matrix and the observation error covariance matrix are respectively used as a model weight and an observation weight, so that the assimilation result simultaneously meets the model constraint and the observation constraint.
Further, the optimal model parameter solution for enabling the cost function to obtain the global minimum value is solved by adopting a global optimization algorithm genetic algorithm, so that the problem that the global optimal assimilation parameter solution cannot be obtained due to the fact that the optimal model parameter solution falls into the local optimal solution of the cost function is solved.
In conclusion, the method can effectively estimate the model parameters, realizes the estimation of the internal structure parameters and the water level of the steam generator, and is beneficial to the maintenance and the operation of the steam generator.
The technical scheme of the invention is further described in detail through the attached drawings.
Drawings
FIG. 1 is a functional diagram of a steam generator data assimilation system in accordance with the present invention;
FIG. 2 is a flow chart of a steam generator data assimilation of the present invention;
FIG. 3 is a flow chart of a model parameter sensitivity analysis of the present invention;
FIG. 4 is a flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
Referring to fig. 1 and 2, the present invention provides a data assimilation system for a steam generator of a pressurized water reactor nuclear power plant, which corrects a simulation trajectory of the steam generator by using observation data and a data assimilation algorithm according to a data assimilation goal of the steam generator, obtains pre-estimation of a water level and structural parameters, and defines an input layer, a pretreatment layer, an operation layer and a storage layer.
The input layer receives data input, including observation data such as water level of an actual nuclear power plant, boundary conditions and structural parameters required by the operation of the steam generator simulation model.
The pretreatment layer includes: the method comprises the steps of processing observation data, analyzing sensitivity and uncertainty of structural parameters which are easy to change in the actual operation process of the steam generator, and establishing an observation operator.
The operation layer comprises: converting the observation data into data consistent with the time of the steam generator simulation model; operating the simulation model to obtain the output track of the model under the same boundary condition; and associating the two data sources through a data assimilation algorithm to obtain an assimilation result of the parameter to be assimilated.
The storage tier system runs to store all the required data.
Referring to fig. 2, the method for assimilating data of a steam generator of a pressurized water reactor nuclear power plant according to the present invention includes the following steps:
s1, analyzing the sensitivity of model parameters;
aiming at the thermal hydraulic process of the steam generator of the nuclear power plant, the heat exchange or resistance parameters of each node of the simulation model of the steam generator are selected as research objects.
The value ranges of the parameters are reasonably set by adopting a global sensitivity analysis SOBOL method based on the actual operation process of the steam generator, as shown in FIG. 3.
Carrying out multi-group combined sampling on the model parameters; and taking the sampling parameters as model input, performing multiple times of model operation, and storing model output data.
And processing the output data of the model to obtain a first-order sensitivity index and a global sensitivity index of each parameter to the water level, and evaluating the sensitivity of each parameter.
S2, simulating a data acquisition process of a real nuclear power plant steam generator in an actual operation process or directly acquiring actual measurement data of the nuclear power plant;
and selecting characteristic data which can indirectly represent relevant parameters of the equipment, wherein the characteristic data comprises water level of the steam generator, pressure drop on the secondary side of the steam generator and circulating flow of the steam generator.
S3, arranging and evaluating the model parameters in the step S1 according to a sensitivity sequence, and selecting the model parameters with obvious influence on output as assimilation model parameters; performing combined sampling on parameters of a model to be assimilated by adopting a Mote-Carlo method, performing multiple model operations by taking the parameters to be assimilated as model input, and collecting model output data; fitting a mapping relation between a parameter to be assimilated and the output of the model by a polynomial, wherein the mapping relation is a water level observation operator, a pressure drop observation operator and a circulation flow observation operator; simultaneously calculating the uncertainty of each parameter to be assimilated and the uncertainty of the interaction of the two parameters to the output of the model, and establishing an uncertainty database;
s4, constructing a cost function;
referring to fig. 4, according to the definitions of the cost function of the variational method, the set value of the model parameter of the steam generator simulation model is used as the background value of the cost function; constructing a background error covariance matrix item of the cost function by using the uncertainty of the parameter to be assimilated in the step S3; constructing an observation error covariance matrix item of the cost function by using the observation data in the step S3; taking the actual observation value in the step S2 as an observation value item of the cost function; and using the observation operator in the step S3 as an observation operator item of the cost function.
And (3) solving an optimal model parameter solution (namely a solution which enables the output of the model to be closest to the observed value) which enables the cost function to obtain a global minimum value by adopting a global optimization algorithm genetic algorithm, carrying out online adjustment on the model parameters of the simulation model, constructing a model certainty coefficient, and evaluating the simulation precision of the model after assimilation.
In another embodiment of the invention, a data assimilation system of a steam generator of a pressurized water reactor nuclear power plant is provided, and the system can be used for realizing the data assimilation method of the steam generator of the pressurized water reactor nuclear power plant.
The analysis module analyzes the sensitivity of the parameters of the steam generator simulation model of the nuclear power plant to obtain a first-order sensitivity index and a global sensitivity index of the parameters of the steam generator simulation model of the nuclear power plant to the water level of the steam generator simulation model;
the simulation module simulates the data acquisition process of a real nuclear power plant steam generator in the actual operation process or directly acquires the actual observation value of the nuclear power plant;
the database module is used for arranging and evaluating parameters of the nuclear power plant steam generator simulation model of the analysis module according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the nuclear power plant steam generator simulation model through a polynomial to serve as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database;
and the assimilation module is used for constructing a cost function by utilizing an uncertainty database established by the database module and an actual observation value obtained by the simulation module, calculating the cost function by using a genetic algorithm to obtain an optimized parameter to be assimilated, bringing the optimized parameter into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence precision.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for operating a data assimilation method, a medium and equipment of a steam generator of a pressurized water reactor nuclear power plant, and comprises the following steps: analyzing the sensitivity of parameters of a simulation model of a steam generator of a nuclear power plant to obtain a first-order sensitivity index and a global sensitivity index of the parameters of the simulation model of the steam generator of the nuclear power plant to the water level of the simulation model of the steam generator; simulating a data acquisition process of a real nuclear power plant steam generator in an actual operation process or directly acquiring an actual observation value of a nuclear power plant; arranging and evaluating parameters of a simulation model of the steam generator of the nuclear power plant according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the simulation model of the steam generator of the nuclear power plant through a polynomial to be used as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database; and constructing a cost function by utilizing the established uncertainty database and the actual observation value, calculating the cost function by using a genetic algorithm to obtain an optimized parameter to be assimilated, bringing the optimized parameter into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence precision.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, the memory space stores one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The computer-readable storage medium can be loaded with one or more instructions and executed by a processor to implement the corresponding steps of the above-described embodiments with respect to the PWR nuclear power plant steam generator data assimilation methods, media, and devices; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of: analyzing the parameter sensitivity of the nuclear power plant steam generator simulation model to obtain a first-order sensitivity index and a global sensitivity index of the nuclear power plant steam generator simulation model parameters to the water level of the steam generator simulation model; simulating a data acquisition process of a real nuclear power plant steam generator in an actual operation process or directly acquiring an actual observation value of a nuclear power plant; arranging and evaluating parameters of a simulation model of the steam generator of the nuclear power plant according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the simulation model of the steam generator of the nuclear power plant through a polynomial to be used as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database; and constructing a cost function by utilizing the established uncertainty database and the actual observation value, calculating the cost function by using a genetic algorithm to obtain an optimized parameter to be assimilated, bringing the optimized parameter into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence precision.
In summary, the data assimilation method and system for the steam generator of the pressurized water reactor nuclear power plant utilize a global sensitivity analysis method (SOBOL) to carry out research on thermal hydraulic parameters of each node of a steam generator simulation model; setting reasonable value ranges of all parameters according to actual operation data and states of the steam generator; based on the strong coupling and nonlinear characteristics of a steam generator simulation model, the interaction effect among model parameters is comprehensively considered, and the SOBOL algorithm is adopted to carry out multiple random combined sampling on the model parameters; the method has the advantages that the observation data are effectively utilized to realize the adjustment of the model parameters, the model which accurately reflects the actual operation characteristics of the steam generator is obtained, meanwhile, the effective estimation of the model parameters can realize the estimation of the internal structure parameters and the water level of the steam generator, and the operation and the maintenance of the steam generator are facilitated.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (7)
1. The data assimilation method for the steam generator of the pressurized water reactor nuclear power plant is characterized by comprising the following steps of:
s1, analyzing the sensitivity of parameters of a steam generator simulation model of a nuclear power plant to obtain a first-order sensitivity index and a global sensitivity index of the parameters of the steam generator simulation model of the nuclear power plant to the water level of the steam generator simulation model;
s2, simulating a data acquisition process of a real nuclear power plant steam generator in an actual operation process or directly acquiring an actual observation value of the nuclear power plant;
s3, arranging and evaluating parameters of the nuclear power plant steam generator simulation model in the step S1 according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the nuclear power plant steam generator simulation model through a polynomial to be used as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database;
s4, taking a model parameter set value of the steam generator simulation model as a background value item in the cost function; constructing a background error covariance matrix item of the cost function by using the uncertainty of the parameter to be assimilated in the step S3; constructing an observation error covariance matrix item of the cost function by using the observation data in the step S2; taking the actual observation value in the step S2 as an observation value item of the cost function; and (3) using the observation operator in the step (S3) as an observation and calculation subentry of the cost function, using the uncertainty database established in the step (S3) and the actual observation value obtained in the step (S2) to construct the cost function, using a genetic algorithm to calculate the cost function, obtaining an optimized parameter to be assimilated, bringing the optimized parameter into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence precision.
2. The method according to claim 1, wherein step S1 is specifically:
performing multi-group combined sampling on model parameters of a simulation model of the steam generator of the nuclear power plant based on the actual operation process of the steam generator by adopting a global sensitivity analysis (SOBOL) method; taking the sampling parameters as the model input of the steam generator simulation model, performing multiple times of steam generator simulation model operation, and storing the calculation output data of the steam generator simulation model; and processing the output data of the steam generator simulation model to obtain a first-order sensitivity index and a global sensitivity index of each parameter to the water level.
3. The method of claim 2, wherein the model parameters select heat exchange or resistance parameters for each node of the steam generator simulation model.
4. The method according to claim 1, characterized in that in step S3, assimilation model parameters are selected; and adopting a Mote-Carlo method to carry out combined sampling on the assimilation model parameters, taking the assimilation model parameters as the model input of the steam generator simulation model, carrying out multiple times of steam generator simulation model operation, and collecting the output data of the steam generator simulation model.
5. The method according to claim 4, characterized in that the model parameters in step S1 are ranked according to sensitivity index, and the heat exchange and resistance parameters with the highest sensitivity index are determined as the model parameters to be assimilated.
6. The method according to claim 1, wherein in step S4, a global optimization algorithm genetic algorithm is adopted to solve an optimal model parameter solution which enables the cost function to obtain a global minimum, online adjustment is performed on steam generator simulation model parameters, a deterministic coefficient of the steam generator simulation model is constructed, and the simulation accuracy of the homogenized model is evaluated.
7. A pressurized water reactor nuclear power plant steam generator data assimilation system, comprising:
the analysis module is used for analyzing the sensitivity of the parameters of the steam generator simulation model of the nuclear power plant to obtain a first-order sensitivity index and a global sensitivity index of the parameters of the steam generator simulation model of the nuclear power plant to the water level of the steam generator simulation model;
the simulation module is used for simulating the data acquisition process of the steam generator of the real nuclear power plant in the actual operation process or directly acquiring the actual observation value of the nuclear power plant;
the database module is used for arranging and evaluating parameters of the nuclear power plant steam generator simulation model of the analysis module according to the sensitivity index, and fitting a mapping relation between the parameters to be assimilated and the output of the nuclear power plant steam generator simulation model through a polynomial to serve as an observation operator; calculating the uncertainty of each parameter to be assimilated and the uncertainty of the output of the parameter to be assimilated on a nuclear power plant steam generator simulation model in a pairwise interaction manner, and establishing an uncertainty database;
the assimilation module takes a model parameter set value of the steam generator simulation model as a background value item in the cost function; constructing a background error covariance matrix item of the cost function by using the uncertainty of the parameter to be assimilated in the database module; constructing an observation error covariance matrix item of the cost function by using observation data in the simulation module; taking the actual observation value in the simulation module as an observation value item of the cost function; and using an observation operator in the database module as an observation operator item of the cost function, using an uncertainty database established by the database module and an actual observation value obtained by the simulation module to construct the cost function, using a genetic algorithm to calculate the cost function, obtaining optimized parameters to be assimilated, bringing the optimized parameters into a steam generator simulation model, and realizing data assimilation after the output water level of the steam generator simulation model reaches convergence accuracy.
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