CN115408861B - Data assimilation method, system and terminal for optimizing reactor operating parameters - Google Patents
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Abstract
The application discloses a data assimilation method, a system and a terminal for optimizing reactor operation parameters, which are based on black box optimization and used for optimizing the reactor operation parameters, and comprise the following steps: constructing the relation between the physical calculation input parameters and the output physical field of the reactor; constructing a response relation between a reactor physical field and a detector; acquiring actual measurement data of a detector; constructing a functional relation between input parameters and theoretical-actual measurement deviation of the detector; and finally obtaining the optimal input parameters through a black box optimization method. According to the application, the reactor experiment, experiment and operation measured data can be effectively utilized, the reactor physical software is coupled, the fine adjustment of the reactor physical calculation input parameters is carried out through the black box optimization technology, and finally, the purpose of more accurate reactor physical calculation is realized.
Description
Technical Field
The application relates to the technical field of nuclear reactor core operation and safety, in particular to a data assimilation method, a system and a terminal for optimizing reactor operation parameters.
Background
The physical computation of a nuclear reactor core is an important means for evaluating the safety-related parameters of a reactor, and is an important technology for evaluating the design and operation level of the reactor and supporting the safe and economic operation of the reactor.
The nuclear reactor core physical calculation data assimilation architecture improves the accuracy of theoretical calculation input parameters and output results by scientifically utilizing measured values, realizes the reactor core design, the starting physical test and the reactor physical calculation closed-loop ecology related to the reactor operation, finally realizes the maximum excavation of reactor physical calculation software and reactor measured data values, and improves the core design and core operation support calculation accuracy.
Data assimilation has two major applications in nuclear reactor core physics calculations: estimation of optimal parameters and physical field reconstruction. The physical calculation result of the reactor always has deviation with the test, and the deviation is reflected by various errors or the combined action of the deviation, and the errors are difficult to disassemble through strict demonstration analysis, but the overall comprises the following aspects: (1) Calculating the input parameter errors of a program, wherein the input parameter errors mainly comprise lumped parameter errors such as coolant inlet and outlet temperature, coolant pressure, boron concentration, rod position, power level, macroscopic burnup and the like; (2) a microscopic section database: errors of nuclide section databases corresponding to various materials are reflected by covariance databases, and are important content of uncertainty analysis; (3) macroscopic section database errors: in the two-step method, the assembly calculates to obtain macroscopic section data, wherein the data is actually only intermediate parameters introduced by a calculation model, has no obvious and accurate physical meaning, and has no important influence on a calculation result due to numerical accuracy; (4) Calculating model boundary conditions and initial condition errors by numerical values; (5) other errors in numerical computation modeling. For the lumped parameters (1) and (4), the boundary conditions and the initial conditions, the dimension of the parameters is smaller, generally in the order of 1-100D, the data assimilation adjustment is carried out according to the actual measurement values of the power inside and outside the reactor or other detectors by a data assimilation method, the accurate estimation of the lumped parameters is realized, and further, the power distribution and other data are obtained by carrying out the core physical calculation by using the more accurate lumped parameters. For higher-dimensional (2) (3) section databases, the common term is section database tuning.
The conventional data assimilation technology is to construct a data assimilation equation, and when solving the optimal input parameters, the optimal input parameters often need to go deep into reactor physical calculation software to solve accompanying equations, or directly solve various related gradient matrices (Jacobian matrices) and second-order gradient matrices (hessian matrices) of the target functional. For reactor physics computing software, the above data assimilation process faces two technical difficulties:
1) The physical calculation flow of the reactor is complex, the transmission process from the input parameters to the measured values is complex, and the transmission matrix from the input parameters to the output measured values is difficult to construct through the displayed expression;
2) The physical calculation software of the industrial reactor is excessively large in volume, and Jacobian matrix and hessian matrix codes required for solving data assimilation are difficult to implant through anatomic software.
Therefore, conventional data assimilation methods for core physical computation input parameter optimization are less practical for engineering.
Disclosure of Invention
The technical problems to be solved by the application are as follows: the conventional data assimilation method for optimizing the reactor core physical calculation input parameters has lower practicability for engineering, and the data assimilation method, the system and the terminal for optimizing the reactor operation parameters are provided for solving the problems, can effectively utilize the reactor experiment, experiment and operation actual measurement data, couple reactor physical software, finely tune the reactor physical calculation input parameters through a black box optimization technology, and finally achieve the purpose of more accurate reactor physical calculation.
The application is realized by the following technical scheme:
a data assimilation method for optimization of reactor operating parameters, comprising the steps of:
s1, constructing a relation between a physical calculation input parameter and an output physical field of a reactor;
s2, constructing a response relation between a reactor physical field and a detector;
s3, acquiring actual measurement data of the detector;
s4, constructing a functional relation between the input parameters and the theoretical-actual measured deviation of the detector;
s5, finally obtaining the optimal input parameters through a black box optimization method.
Further optionally, in step S1, the relation between the input parameter and the output physical field is calculated by the reactor physically:
F(μ)＝M F (μ)；
where F (μ) represents the physical field, M represents given core physics calculation software, and μ represents the input parameter.
For a given core physics calculation software M, input parameters needing to be optimized are determined, p parameters are set, and the corresponding parameters are recorded as mu= (mu) 1 ，μ 2 ，...，μ p ) T ∈R p The other input parameters are fixed input parameters, q are provided, and the corresponding parameters are marked as upsilon= (upsilon) 1 ，υ 2 ，...，υ q ) T Reactor types of data calculated by M, including fastHeat mass flux distribution phi 1 (r)，φ 2 (r), power distribution P (r), various types of reactivity ρ, and security-related parameters FQ, FDH, LPD and the like can also be calculated from the power distribution P (r). For flux, power, etc. distribution type physical field F (r, μ, v) (abbreviated as F (μ)) given an input parameter (μ, v), there is the following relationship
F(μ)＝M F (μ，υ) (1)；
Considering the fixed v, the above formula can be further abbreviated as:
F(μ)＝M F (μ) (2)。
further optionally, in step S2, the response relationship between the reactor physical field and the detector is:
Y(μ)＝Y(F(μ))＝l(M F (μ))；
wherein Y (mu) represents theoretical measurement values corresponding to M detectors arranged inside and outside the reactor core, l (M) F (μ)) represents the response of the m detectors to the current core internal physical field F (μ).
For given reactor experimental, experimental and operational measured data, a total of m detectors are provided for the in-core arrangement, each detector having a response to the current in-core physical field F (μ) expressed as l j (F (μ)), i.e. the theoretical measurement y corresponding to the j-th detector j (F (μ)) can be expressed as:
y j (μ)＝y j (F(μ))＝l j (F(μ)) (3)；
the measured values of the m detectors are written in a matrix form:
Y(μ)＝Y(F(μ))＝l(F(μ)) (4)；
wherein,in connection with equation (2), the relationship of the measured value to the parameter can be written as:
Y(μ)＝Y(F(μ))＝l(M F (μ)) (5)；
note that: the detector response function is an inherent indicator of the detector and can be considered a known quantity. Thus, given an input parameter μ, F (μ) can be calculated by the core physics calculation program according to equation (2), and Y (μ) can be calculated by the detector response function according to equation (5).
Further optionally, in step S3, the measured data of the detector is:
Y o ＝Y(μ)+∈＝l(M F (μ))+∈；
where Yo represents the actual measured data for m detectors and e represents the random error to which m detectors respond.
Obtaining experimental, experimental and operational measured data for a given reactor, the actual measured data for m detectors is noted as Y o . Taking into account each detector y j Responsive random error e j The actual measured value is related to the theoretical value of the detector:
thus, the first and second substrates are bonded together,
Y o ＝Y(μ)+∈＝l(M F (μ))+∈ (7)。
further optionally, in step S4, the detector theoretical-measured deviation is:
the detector theory-measured deviation is defined by:
the method is characterized by comprising the following steps:
at the time of obtaining measured value Y o Given the input parameter μ, f (μ) can be calculated by equation (9).
Further alternatively, in step S5, the best is finally obtained by a black box optimization methodOptimal input parameter mu op ：
Further alternatively, the optimal input parameter μ is calculated op The method of (1) comprises:
given a maximum number of iterations k max And convergence criterion epsilon=c (m) σ; wherein sigma is the mean square error of the detectors, and c (m) is a coefficient related to the number m of the detectors;
s51, determining initial input parameters mu 0 ：
μ 0 Is an input parameter of the physical calculation of the reactor in the current running state of the reactor; some of which are measured by actual operation, such as reactor coolant inlet and outlet temperatures, coolant pressures, power levels, burnup levels, etc., and some of which are reactor design parameters, such as core size, boundary conditions, etc.
Let k=1, μ k ＝μ 0 The method comprises the steps of carrying out a first treatment on the surface of the When k is<k max Or f k S52 is performed when < epsilon, otherwise S53 is performed;
s52, for the kth iteration, determining mu k Delta mu of (2) k ，f(μ k ±Δμ k )；
Because of mu k The increment is considered to be carried out in each dimension respectively, and other dimensions are unchanged, so the calculation needs to be carried out 2 times, mu corresponding to the minimum value is the optimal value of the iteration, and is recorded as mu k+1 ；
Let k=k+1;
s53. Output mu op ＝μ k 。
In step S52, any other gradient-free optimization method may be used instead.
Further optionally, the method further comprises step S6: and performing reactor physical calculation again by obtaining the optimal input parameters to obtain various calculated values.
A black box optimization-based data assimilation system for reactor operating parameter optimization, comprising:
the first construction module is used for executing the step S1 and constructing the relation between the input parameters and the output physical field of the reactor physical calculation;
the second construction module is used for executing the step S2 and constructing the response relation between the reactor physical field and the detector;
the acquisition module is used for executing the step S3 and acquiring actual measurement data of the detector;
the third construction module is used for executing the step S4 and constructing a functional relation between the input parameters and the theoretical-actual measured deviation of the detector;
and the calculation module is used for executing the step S5, and finally obtaining the optimal input parameters through a black box optimization method.
An electronic terminal, comprising:
a memory for storing a computer program;
and the processor is used for executing the computer program stored in the memory so as to enable the electronic terminal to execute the data assimilation method for optimizing the reactor operation parameters.
The application has the following advantages and beneficial effects:
the application uses reactor design or operation parameters as input, and uses reactor core physical calculation software to make core physical calculation to evaluate reactor design and operation performance, and uses experimental, experimental or operation actual measurement data to make optimization of input parameters. The reactor physical software is coupled through effectively utilizing the reactor experiment, experiment and operation measured data, a gradient irrelevant data assimilation frame is established, the search direction and the convergence criterion are reasonably selected through a gradient-free black box optimization technology, the fine adjustment of the reactor physical calculation input parameters is carried out, and finally the purpose of more accurate reactor physical calculation is realized.
The application is realized by optimizing the parameter mu op The physical quantity obtained by performing the physical calculation of the reactor is improved by directly calculating the physical quantity through the initial input parameters without performing parameter optimization, and the error is in the same magnitude as the error of the detector. The optimized objective function adopted by the application can effectively inhibit the measurementAnd measuring noise.
The application adopts the gradient-free data assimilation technology of the front edge, greatly utilizes theoretical calculation and actual measurement information, and realizes high-precision reactor core physical calculation and input parameter optimization. The application can be applied to the support simulation of the operation of the Hua-Lou reactor, can be popularized and applied in the aspects of the design and operation of other novel reactors, and has wide application prospect and economic and social benefits.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart of the parameter optimization direction selection according to the present application.
FIG. 2 is a "Hua Longya" 1/4 core layout, with 'D' representing the assembly or segment where the detector is located.
Fig. 3 shows the layer 7 power distribution bias obtained from the initial input parameters.
Fig. 4 shows the layer 7 power distribution bias obtained from the optimized input parameters.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the application. In other instances, well-known structures, circuits, materials, or methods have not been described in detail in order not to obscure the application.
Example 1
The embodiment provides a black box optimization-based data assimilation method for optimizing reactor operating parameters, which comprises the following specific steps:
(1) The relation between input and output is calculated physically by building up reactor.
For a given core physics calculation software M, input parameters needing to be optimized are determined, p parameters are set, and the corresponding parameters are recorded as mu= (mu) 1 ，μ 2 ，...，μ p ) T E Rp, the rest input parameters are fixed input parameters, q are provided, and the corresponding parameters are marked as upsilon= (v) 1 ，υ 2 ，...，υ q ) T . Reactor class data calculated by M, including fast heat group flux distribution phi 1 (r)，φ 2 (r), power distribution P (r), various types of reactivity ρ, and security-related parameters FQ, FDH, LPD and the like can also be calculated from the power distribution P (r). For a flux, power etc. distribution class physical field F (r, μ, v) (abbreviated as F (μ)) given an input parameter (μ, v), there is the following relationship:
F(μ)＝M F (μ，υ) (10)；
considering the fixed v, the above formula can be further abbreviated as:
F(μ)＝M F (μ) (11)。
(2) And constructing the response relation between the reactor physical field and the detector.
For given reactor experimental, experimental and operational measured data, a total of m detectors are provided for the in-core arrangement, each detector having a response to the current in-core physical field F (μ) expressed as l j (F (μ)), i.e. the theoretical measurement y corresponding to the j-th detector j (F (μ)) can be expressed as:
y j (μ)＝y j (F(μ))＝l j (F(μ)) (12)；
writing the measured values of m detectors in matrix form
Y(μ)＝Y(F(μ))＝l(F(μ)) (13)；
Wherein,in connection with equation (2), the relationship of the measured value to the parameter can be written as:
Y(μ)＝Y(F(μ))＝l(M F (μ)) (14)；
note that: the detector response function is an inherent indicator of the detector and can be considered a known quantity. Thus, given an input parameter μ, F (μ) can be calculated by the core physics calculation program according to equation (2), and Y (μ) can be calculated by the detector response function according to equation (5).
(3) And obtaining the actual measurement data of the detector.
Obtaining experimental, experimental and operational measured data for a given reactor, the actual measured data for m detectors is noted as Y o . Taking into account each detector y j Responsive random error e j The actual measured value is related to the theoretical value of the detector:
thus, the first and second substrates are bonded together,
Y o ＝Y(μ)+∈＝l(M F (μ))+∈ (16)。
(4) And constructing a function relation between the input parameters and the theoretical-actual measured deviation of the detector.
The detector theory-measured deviation is defined by:
Yo22
the method is characterized by comprising the following steps:
at the time of obtaining measured value Y o Given the input parameter μ, f (μ) can be calculated by equation (9).
(5) Finally obtaining the optimal input parameter mu by a black box optimization method op 。
The general optimization concept can be simply described as
The specific algorithm is as follows:
given a maximum number of iterations k max And convergence criterion epsilon=c (m) σ. Where σ is the detector mean square error and c (m) is a coefficient related to the number of detectors m.
(5.1) determining the initial input parameter μ 0 。
μ 0 Often, the input parameters of the physical calculation of the reactor in the current operating state of the reactor are partly measured through actual operation, such as the inlet and outlet temperature of the reactor coolant, the pressure of the coolant, the power level, the burnup level, etc., and partly the design parameters of the reactor, such as the core size, the boundary conditions, etc.
Let k=1, μ k ＝μ 0 . When k is less than k max Or f k Execution (5.2) when < ε, otherwise execution (5.3)
(5.2) for the kth iteration, determine μ k Delta mu of (2) k ，f(μ k ±Δμ k )。
Because of mu k The increment is considered to be carried out in each dimension respectively, and other dimensions are unchanged, so the calculation needs to be carried out 2 times, mu corresponding to the minimum value is the optimal value of the iteration, and is recorded as mu k+1 . The parameter optimization direction selection flow chart is shown in fig. 1.
Let k=k+1.
(5.3) output mu o p ＝μ k 。
Note that: step (5.2) may be replaced by any other gradient-free optimization method.
(6) By obtaining the optimal input parameter mu op The reactor physical calculation is performed again to obtain various calculated values.
Example 2
The present embodiment provides a black box optimization-based data assimilation method for reactor operating parameter optimization, and the application of the solution based on embodiment 1 is as follows:
modeling was performed for the Huaronone number (HPR 1000) reactor core (see FIG. 2) power, considering four main factors affecting the power distribution:
-St: inserting the control rod group from 0 to 615;
bu: burnup, unit MWd/tU from beginning of life (BOC) to end of life (EOC);
-Pr: power level, 0-100%;
-Tin: the coolant inlet temperature is 290-300 ℃.
Thus, the parameter μ= (St, bu, pr, tin). The power distribution and other safety-related parameters in each case within the parameter space are obtained using SCIENCE/CORCA-3D.
Two different sets of initial conditions, mu, are selected 01 = (20,100,61.11,291.76) and μ 02 = (210,600,71.51,293.54). Assuming that the corresponding real states are μ respectively t1 = (0,0,61.11,291.76) and μ t2 ＝(200,500,73.51,290.54)。
Before parameter optimization, the deviation between the calculated axial 7 th layer power distribution and the actual power distribution is shown in fig. 3, and the average error of the three-dimensional power distribution is 8.41% and 2.92%, respectively. The input parameter optimized by the application is mu op2 = (0.0,17.7,58.30,287.40) and μ op2 After the parameter optimization, the deviation between the calculated axial 7 th layer power distribution and the actual power distribution is shown in fig. 4, and the average error of the three-dimensional power distribution is 0.63% and 0.93%, respectively.
Therefore, the numerical result proves that the parameter optimization based on the measured data is realized by constructing the data assimilation framework with the practical engineering significance.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the application, and is not meant to limit the scope of the application, but to limit the application to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (4)
1. A data assimilation method for optimization of reactor operating parameters, comprising the steps of:
s1, constructing a relation between a physical calculation input parameter and an output physical field of a reactor, wherein the relation between the physical calculation input parameter and the output physical field of the reactor is as follows:
F(μ)＝M F (μ)；
wherein F (mu) represents a physical field, M represents given core physical calculation software, and mu represents an input parameter;
s2, constructing a response relation between a reactor physical field and a detector, wherein the response relation between the reactor physical field and the detector is as follows:
Y(μ)＝Y(F(μ))＝l(M F (μ))；
wherein Y (mu) represents theoretical measurement values corresponding to M detectors arranged inside and outside the reactor core, l (M) F (μ)) represents the response of the m detectors to the current core internal physical field F (μ);
s3, acquiring actual measurement data of a detector, wherein the actual measurement data of the detector are as follows:
Y o ＝Y(μ)+∈＝l(M F (μ))+∈；
wherein Y is o Representing actual measurement data for m detectors, e representing random errors for which the m detectors have responses;
s4, constructing a functional relation between input parameters and detector theory-actual measurement deviation, wherein the detector theory-actual measurement deviation is as follows:
s5, finally obtaining optimal input parameters through a black box optimization method;
wherein, the optimal input parameter mu is finally obtained by a black box optimization method op The method comprises the following steps:
wherein R is p Representing a p-dimensional real set;
calculating to obtain optimal input parameter mu op Method package of (2)The method comprises the following steps:
given a maximum number of iterations k max And convergence criterion epsilon=c (m) σ; wherein sigma is the mean square error of the detectors, and c (m) is a coefficient related to the number m of the detectors;
s51, determining initial input parameters mu 0 ：
μ 0 Is an input parameter of the physical calculation of the reactor in the current running state of the reactor;
let k=1, μ k ＝μ 0 The method comprises the steps of carrying out a first treatment on the surface of the When k is less than k max Or f k <S52 is performed when epsilon, otherwise S53 is performed;
s52, for the kth iteration, determining mu k Delta mu of (2) k ，f(μ k ±Δμ k )；
Because of mu k The increment is considered to be carried out in each dimension respectively, and other dimensions are unchanged, so the calculation needs to be carried out 2 times, mu corresponding to the minimum value is the optimal value of the iteration, and is recorded as mu k+1 ；
Let k=k+1;
s53. Output mu op ＝μ k 。
2. The data assimilation method for reactor operating parameter optimization according to claim 1, further comprising step S6: and performing reactor physical calculation again by obtaining the optimal input parameters to obtain various calculated values.
3. A black box optimization-based data assimilation system for reactor operating parameter optimization, for implementing the data assimilation method for reactor operating parameter optimization of claim 1, comprising:
the first construction module is used for executing the step S1 and constructing the relation between the input parameters and the output physical field of the reactor physical calculation;
the second construction module is used for executing the step S2 and constructing the response relation between the reactor physical field and the detector;
the acquisition module is used for executing the step S3 and acquiring actual measurement data of the detector;
the third construction module is used for executing the step S4 and constructing a functional relation between the input parameters and the theoretical-actual measured deviation of the detector;
and the calculation module is used for executing the step S5, and finally obtaining the optimal input parameters through a black box optimization method.
4. An electronic terminal, comprising:
a memory for storing a computer program;
a processor for executing the computer program stored in the memory to cause an electronic terminal to execute the black box optimization-based data assimilation method for reactor operating parameter optimization according to claim 1 or 2.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108062988A (en) * | 2016-11-09 | 2018-05-22 | 国家电投集团科学技术研究院有限公司 | Reactor physics start method and system |
| CN113409975A (en) * | 2021-06-17 | 2021-09-17 | 中国核动力研究设计院 | Reactor core power distribution monitoring method and system based on model order reduction and data assimilation |
| CN113421669A (en) * | 2021-06-17 | 2021-09-21 | 中国核动力研究设计院 | Reactor core power distribution online reconstruction method and system based on local nonlinear correction |
| CN114186405A (en) * | 2021-12-02 | 2022-03-15 | 中国人民解放军92578部队 | Parameter uncertainty analysis method and system of nuclear power reactor system |
| CN114266157A (en) * | 2021-12-22 | 2022-04-01 | 中国核动力研究设计院 | Method, device and equipment for realizing reactor power distribution test at any moment |
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