JP2013065213A - Simulation result correcting device and correcting method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a simulation result correcting device which corrects errors contained in a simulation result due to parameters by using a correction coefficient quantitatively estimated by using an experimental result simulating an objective system.SOLUTION: A simulation result correcting device includes: a relative difference between calculated value and measured value storage section 144; a covariance error matrix calculating section 111 for calculating influence of uncertainty of an input value; an actual apparatus system sensitivity coefficient vector calculating section 117 and an experimental system sensitivity coefficient vector calculating section 115 for calculating the influence of the input value; a first linear combination constant calculating section 118 for calculating a linear combination constant αand a linear combination constant storage section 148 for storing the calculated linear combination constant αtherein; a simulation evaluation factor calculating section 119 for calculating a simulation evaluation factor RF and a simulation evaluation factor storage section 150 for storing the calculated simulation evaluation factor RF therein; a correction index value calculating section 120 for estimating a correction index value of an object physical quantity; a reliability enhancement factor calculating section 121 for calculating a reliability enhancement factor RCF from a value of the simulation evaluation factor RF; and a calculation result correcting section 122.

Description

  In the present invention, an error included in a simulation result using a model representing a physical phenomenon of an object on a computer is estimated, an error is estimated using an experimental result simulating the object, and a calculation based on the estimated error is performed. Regarding correction of results.

  It is necessary to confirm whether the product manufactured in the industrial field can exhibit the target performance before actually using it. The most frequently used method is a method in which almost the same product as the final product is made and the performance of the prototype is confirmed, which is called a verification test. For example, prototypes are manufactured for home appliances, cars, and railroad trains, their performance is confirmed, quality is guaranteed, and they are shipped as final products.

  On the other hand, in a specific industrial field, the size of a product to be manufactured may be huge, and it may be extremely difficult to confirm the performance of the final product by trial manufacture. Also, it may not be reasonable to produce a prototype because the product to be manufactured is very expensive, has only one or a few, or is economical or for some other reason.

  For example, in actual nuclear power plants and facilities related to nuclear materials, it is almost impossible to make prototypes of these power plants and facilities economically and physically. One reason is that the nuclear material to be used cannot be freely prepared. When constructing a spent nuclear fuel storage facility, it is practically impossible to prepare the same spent nuclear fuel stored in advance and measure the performance of the facility. Even when constructing a nuclear power plant, preparing a separate fuel assembly in advance and confirming the nuclear performance of the core with the nuclear fuel is hardly feasible from an economic point of view.

  Therefore, in the nuclear field, performance has not been confirmed using actual products, but performance has been confirmed by calculation since the birth of the industrial field. Also, in other fields, especially in the design of expensive products such as aircraft, rockets, large ships, etc., it is possible to grasp the final performance by calculation at the design stage in order to reduce the cost of the product and shorten the production time, It is becoming more and more common.

  A computer is generally used for the prediction calculation. The technology for grasping the performance of a “product” by a computer program created based on the physical theory followed by the product is called computational physics, calculation experiment, computer simulation (computer simulation), etc., and has become a greatly developed technical field. ing.

  When constructing a nuclear power plant, it is impossible to check the nuclear performance of the reactor core by preparing an actual fuel assembly at the design stage, so the performance is confirmed by computer simulation. The number of fuel assemblies loaded in the reactor core and the concentration and distribution of nuclear material contained in the fuel assemblies are also determined based on computer simulation. Such computer simulation technology is indispensable in the nuclear field.

  Computer simulation is the accumulation of numerical calculations, and there is always a calculation error. The certainty of calculation is called calculation accuracy, but grasping calculation accuracy is very important for improving design reliability and safety, improving economy, and rationalizing design. Accurately grasping the calculation accuracy has the same meaning as accurately grasping the calculation error associated with the obtained calculation value.

  Next, the types of calculation error will be described.

  There are several types of error factors in the calculation. It is an error caused by a calculation method error, a numerical calculation error, and a numerical value (input value) used for the calculation.

  The physical model used for the calculation is merely a mathematical model of a physical phenomenon in the natural world, and the natural world does not change according to this physical model. In addition, an error occurs when the physical model is approximated or deformed so that it can be calculated by a computer. These are errors in the calculation method.

  In nuclear calculations, the basic equation of neutron motion is described by the Boltzmann transport equation. When Boltzmann's transport equation is solved by a computer, it is solved using some approximation. In reactor core calculations, diffusion equations that approximate Boltzmann's transport equation are widely used. This diffusion equation approximates Boltzmann's transport equation ignoring information on the angular direction of neutron motion. Therefore, in a system where the direction of neutron motion is not uniform, there is a risk that the error will increase in the calculation by the diffusion equation. In addition, the diffusion coefficient used in the diffusion equation may not be able to set an appropriate numerical value for a medium having a low density, which causes an error. If the system to be calculated is small, that is, if a small core with a large neutron leakage rate is calculated by diffusion theory, there is a tendency to overestimate the leakage and underestimate the critical eigenvalue, which tends to increase the calculation error. It is said that there is.

  As described above, when performing the calculation, it is necessary to examine whether the theory or model used for the calculation is appropriate or an error occurring in the calculation method.

  The numerical calculation error is an error caused by the four arithmetic operations in the computer calculation process. Typical numerical calculation errors include, for example, rounding errors and round-up / round-down errors. However, the numerical calculation error is sufficiently negligible due to the development of the presently devised numerical calculation technology and computer hardware, and is generally sufficiently small compared to other errors. Absent.

  What is considered to be the largest cause of error is an error caused by a numerical value used for calculation. The numerical values used for the calculation include a numerical value unique to the calculation target such as a dimension, a substance constituting the system, and a number density of the substance, and a numerical value commonly used for the calculation without depending on each calculation target. For example, if the temperature and pressure of water are determined, the density of water is determined, but the density value is a commonly used value without depending on the calculation system. All these numerical values used for the calculation will be called parameters hereinafter. An error caused in the calculation result by the parameter is hereinafter referred to as a parameter error.

  Commonly used physical constants are not true values and contain errors. However, the accuracy of the physical constant is much higher than other numerical values, and there are many significant figures exceeding 6 digits. For this reason, in general, it can be determined that errors in physical constants do not pose a problem in the design and construction of nuclear facilities and the design and production of fuel assemblies.

  On the other hand, the parameters that have been judged to be the most important in the nuclear calculation so far are numerical values related to the “nuclear data library”, such as neutron reaction cross section, decay constant, yield, delayed neutron ratio. The “nuclear data library” is a group of numerical values that are directly related to nuclear calculations, and these numerical changes have a great influence on the calculated values indicating the nuclear characteristics. In addition, the “nuclear data library” originally does not have all values accurately confirmed / determined by measurement, but also includes numerical values determined by theoretical calculations. For this reason, it is determined that the error included in the “nuclear data library” is larger than other parameter errors. Therefore, in the nuclear calculation, the parameter error of the “nuclear data library” is regarded as important.

  The next most important parameter error is the error in atom number density. In the nuclear power field, the atomic density is obtained by combustion calculation for fuel assemblies after operation. For this reason, when the atomic number density is used in the subsequent calculation, it is determined that a calculation error is already included in the atomic number density.

  In the nuclear power industry, it may be difficult or unrealistic to confirm and measure the composition of the products handled, such as the constituent elements and atomic density of the elements, with high accuracy. The nuclide composition and the atomic density of the nuclide in the fuel assembly are extremely important for determining the reactivity when the fuel assembly is used next, and for quantifying critical safety and radiation dose during storage and transportation. It is a numerical value. However, it is not practical to remove the fuel assemblies from the core for the measurement of the nuclide composition and atomic number density, and to analyze them by breaking them. Moreover, each fuel assembly with a high radiation level is one by one. Accurate measurement from the outside is not feasible both technically and economically.

  Therefore, the nuclide composition and atomic density of the fuel assembly are calculated as accurately as possible by grasping the environment in which the past fuel assembly has been placed as accurately as possible. This is called combustion calculation. The atom number density thus obtained includes a calculation error. This atomic density also has a large effect on the calculation results of nuclear calculations using this value as input, and is important when considering the causes of errors in the calculations.

  When performing calculations related to actual nuclear power plants, the flow rate, temperature, and material temperature of the fluid moving in the nuclear reactor may not provide accurate values for the number of input points required for the calculation. Assuming typical numerical values are entered for calculation. In the core calculation of a boiling water reactor (BWR) in which a two-layer flow of water and steam exists in the core, the calculation error caused by the flow rate error is considered to be a significant value.

  Since the beginning of the nuclear industry, experimental devices have been created and used to confirm the nuclear-specific phenomenon of nuclear material criticality. One of them is a critical experiment device. A critical experiment apparatus is generally an apparatus designed to be operated and operated at normal temperature (room temperature) under atmospheric pressure.

  The critical experimental device is a small nuclear reactor that achieves a critical state by using nuclear materials such as uranium and plutonium used in an actual nuclear reactor. However, since the device is very small, it generates little heat. It is a device such as a miniature version of a nuclear reactor and is called a critical experiment device because it can achieve a critical state. Experiments using critical experiment equipment are called critical experiments.

  Criticality experiments have been used to reduce computational errors. The purpose of the critical experiment is to assemble the system under conditions that are physically very simplified and simplified, and at the same time acquire highly accurate measurement data. A physically very simple and simplified system can reduce errors in calculation input parameters related to shape and composition, and a measured value with a small error facilitates comparison with a calculated value. From the past to the present, many measured values obtained by critical experiments were compared with calculated values in order to clarify “errors caused by calculation methods” and “errors caused by nuclear data libraries”. As a result, error factors have been identified and errors have been quantified.

  In addition, if the measured values of the critical experiment can be reproduced well, it is judged that the quality of the parameters used in the calculation, such as the nuclear data library used in the calculation, is high. If it is judged that the quality is high, it has become a quality assurance / judgment criterion that the target system may be designed by the combination of the same method and parameters.

  The most important thing in the critical experiment is the critical condition. Therefore, the critical conditions are accurately acquired as data. In terms of reactor physics, the critical condition is sometimes expressed by the term “critical mass”. This is not only the nuclear material type and mass, but also the geometry of the system, the temperature, the composition and mass of the nuclear material. The exact number of

  The next important thing is the spatial distribution of the fission reaction when the critical experimental device becomes critical. In a critical experiment apparatus configured by combining fuel rods, the radiation emitted from the fuel rods is often measured, and the distribution of the fission reaction is measured by the ratio of the radiation doses. This is because the fission reaction and the amount of radiation emitted are considered to be proportional. In many cases, a special radiation measuring instrument such as a small fission ionization chamber is inserted into the critical experimental apparatus to measure the amount of radiation at the target position. Also in this case, the distribution of neutron flux and the like are determined by the ratio of measured values.

  Once the desired measurement value is obtained, the calculation is performed with the critical experiment system and experimental conditions as inputs. Comparing the calculated value with the critical value obtained in the critical experiment and the measured values of fission reaction distribution, neutron flux distribution, etc., the quality of the entire calculation can be grasped. In other words, if the calculated value and the measured value agree with each other within a range of measurement error and an acceptable level, the computer program (calculation method) that calculated the critical experiment and the parameters used in the calculation centered on the nuclear data library have sufficient quality. It becomes the ground that it has. After that, the same computer program (calculation method) and nuclear data library are used to determine that it can finally be applied to the calculation of the design of the target reactor or reactor facility.

  On the other hand, if there is a significant difference between the calculated value and the measured value, identify the problems in the calculation method and nuclear data library and the points that need to be improved, and the improvements will be made to improve the consistency between the calculated value and the measured value. Made. Thus, criticality experiments have contributed to the confirmation and guarantee of the quality of computers (programs) and nuclear data libraries, or to improvements in calculation methods and nuclear data libraries.

  The numerical value (measured value) obtained in the criticality experiment also includes an error. The error is called measurement error. The measurement error includes a statistical error (random error, statistical error) that occurs by chance during each measurement, and an error (systematic error) associated with the measuring instrument or method used for the measurement. Statistical errors have the property of following the probability distribution and increasing the number of measurements to reduce the error rate. Since the systematic error is an error accompanying the measurement method itself, it always occurs every measurement, and the systematic error rate does not change depending on the number of measurements. Note that the measurement error ratio is considered to be smaller than the calculation error ratio.

  In general, the following two applications have been made as methods of using data obtained in critical experiments. These are the correction factor (bias) method and cross-sectional area adjustment.

  The correction factor (bias) method aims to reduce the relative error caused by calculation in the target system, and does not consider removing the cause of the error.

  Another method for improving the calculation accuracy of the target system using the calculated value and the measured value of the critical experiment is a cross-sectional area adjustment. Considering the cause of the calculation error as the nuclear data library (cross-section library), adjust and correct (adjust) the nuclear data library values so that the calculated values and the measured values in the critical experiment match. The idea is to use the nuclear data that has been performed to calculate the target system and improve the calculation accuracy.

  Patent document 1 mainly evaluates errors included in calculated values, such as geometrical dimensional errors / tolerances, boundary conditions, and numerical analysis errors using sensitivity coefficients when calculating fluids. A technique is disclosed. Non-Patent Document 1 discloses a method of correcting a calculated value by multiplying a calculated value by a bias when obtaining a final design value for a target system. In this method, a general bias is obtained by combining the ratio between the measured value and the calculated value obtained in the experiment.

  The method described in Patent Document 1 uses only calculated values to the last, and there is no means for effectively using measured values obtained by experiments. In addition, although the method described in Non-Patent Document 2 describes a mathematical method, the calculation is extremely complicated because an exponential function is used, and a coefficient necessary for each experiment cannot be calculated uniquely and simply. Furthermore, as a basic problem, there is no physical reason why the exponential function should be used in the calculation algorithm, so the basis of the theory was not sufficient.

  In order to solve this point, Patent Document 2 discloses an invention of an error estimation device based on an error estimation method with theoretical support.

  On the other hand, in the actual design, a design with a margin is made to ensure that the performance required by the product is satisfied. For example, when designing an automobile with a maximum speed of 100 km / h, if the error expected in the design calculation is 6%, the reliability is considered to be 94% (0.94). / H (= 100 ÷ 0.94). Thus, when manufacturing an actual industrial product, the design calculation value has been designed by adding or subtracting the tolerance or the safety factor, or by multiplying it.

  This is a technique that has been often used in the design of the building field. For example, by multiplying the minimum required value by 2 for the strength of buildings required by earthquakes, etc., it has twice the strength in design. May be designed to be The same applies to the cables that suspend the elevator, and it is designed and manufactured to withstand the maximum number of people that can enter the elevator or several times the weight that is carried.

  The same applies to the nuclear field. In the field of criticality safety related to criticality, the most important point regarding nuclear performance, it is critical that the (effective) multiplication factor of neutrons is 1.0. In order to prevent products such as fuel assemblies and nuclear facilities from becoming critical, the neutron multiplication factor is designed to be less than 0.95 in the design calculation. These are considerations to prevent the actual product and the implementation facility from finally reaching a critical state even if there is a calculation error of 5% in the design calculation.

  Historically, these margins and safety factors are (1) set by governments and international public institutions, (2) determined by past experience and methods that have been used for a long time (3 ) The numerical value is determined by the personal judgment of the engineer, etc., and has been added or multiplied as a calculation error.

  Even when applying the calculation error clarified in the experiment for the performance of interest to the design calculation value in some form, past experience, methods that have been used for a long time, engineers' A value called a safety factor is determined separately by personal judgment, and has been used by being further added to or subtracted from the calculated calculation error.

  For example, in speculative statistics, a value of 95% confidence interval is often used, and a value of 5% is often used as a criterion. In the nuclear power field, the numerical value of 0.95 is often used as a criterion for critical safety, and is applied to other designs. This is an example in which a value of 5% is unconditionally used as a safety factor by subtracting from a critical value of 1.000.

  Certainly, it is an important concept to ensure safety after manufacturing an industrial product after evaluating the safety side as a design value using these safety factors. On the other hand, an excessive safety factor is not a sensible method in terms of economy because the design margin is overestimated beyond the required range. For example, it is economically disadvantageous to produce a structure that is sufficiently strong when assembled with 100 kg of steel using 150 kg of steel. Therefore, the application of an excessive safety factor is an economically optimal design. Will not bring.

  As mentioned above, when using the calculation error that can be evaluated as the difference between the measured value of the (critical) experiment and the value obtained by calculating the experiment with the design code, (Critical) The value of the calculation error obtained is high enough that the experiment is sufficiently simulated in the target system. In other words, it is clear that the obtained calculation error is highly applicable to the calculated value of the target actual machine system.

  However, until now, the same safety factor has always been used regardless of the degree of similarity of experimental systems.

  Therefore, although it can be said that sufficient consideration has been given to safety, no consideration has been given to the optimization of the safety factor, including economic aspects, in handling the safety factor.

  (Critical) The calculation error can be grasped from the difference between the measured value obtained by conducting the experiment and the calculated value obtained by calculating the experiment with the design code. When applying this calculation error to the design value of the target actual system, it has been used for a long time without considering the degree of similarity of the (critical) experiment performed to the target actual system. According to the method, a safety factor is selected, and a calculation error and the safety factor are combined and applied to the calculated value of the design calculation.

  Although this is sufficient as a method for ensuring safety, a constant value is usually used for the safety factor, and it has been used uniformly for both those with good design accuracy and those that are not. This is a simple method that does not require confirmation of the design accuracy every time, but there is a case where the margin is excessively set, and when considering the economic aspect as an industrial product, it is not necessarily optimally handled. I can't say that.

  The inventions disclosed in Patent Documents 1 and 2 are intended to correct the original design calculation values using the estimated estimation results of errors, and the performance of the target actual system is particularly human. If it is not directly related to the safety of social activities, this is a sufficient technique that can be completed. On the other hand, for example, the reliability of design is directly related to human social activities, such as calculating the strength of an aircraft body. In such cases, a safety factor has been applied in addition to the calculated design value. Although the viewpoint of combining such safety factors is not described in Patent Documents 1 and 2, it does not deny the contents of these proposals, and is a concept that supplements design calculation values.

  In addition, when the target actual system is a specific product or facility, it is necessary to set an appropriate margin for calculation error (uncertainty) in order to maintain the final accuracy. For example, a facility that stores nuclear materials and fuel assemblies and maintains subcriticality. In these facilities, maintaining subcriticality is the most important issue. To increase the degree of safety, the total amount of nuclear material and fuel assemblies should be reduced, but this is the opposite for the purpose of economic operation of equipment and facilities. Therefore, a highly accurate evaluation of the degree of subcriticality is strongly demanded from the viewpoint of both economic efficiency and criticality safety.

  For many years, many safety factors and correction factors have been used to ensure subcriticality. However, these values and methods were not determined based on a sufficient theoretical background, and many were determined by the experience and judgment of engineers.

JP 2008-217139 A JP 2011-106970 A

Tadafumi SANO and 1 other, "Generalized Bias Factor Method for Accurate Prediction of Neutronics Characteristics", Journal of NUCLEAR SCIENCE and TECHNOLOGY, Vol.43, No.12, Page 1465-1470, 2006 Teruhiko KUGO and two others, "Theoretical Study on New Bias Factor Methods to Effectively Use Critical Experiments for Improvement of Prediction Accuracy of Neutronic Characteristics", Journal of NUCLEAR SCIENCE and TECHNOLOGY, Vol.44, No.12, Page 1509-1517, 2007

  In the field of nuclear power, or in the field where it is not practical to confirm the final performance by producing a prototype, based on a specific design method, calculate the physical quantity of interest of the target system using specific parameters as input. The design value.

  The equation used in this calculation is called a model equation here. Quantitative evaluation of numerical errors included in design values, which are calculated values, at the stage of design calculation is an extremely important task for confirming design accuracy.

  Criticality experiments have been conducted in the nuclear field to confirm design accuracy. Measure the physical quantity of interest in the experiment and perform the calculation using the same calculation method (computer program) and physical constants (parameters) as used in the design, and use the difference between the calculated value and the measured value in the design calculation. Guess the error.

  Alternatively, if the difference between the calculated value and the measured value is within an allowable limit range, it has been determined that there is no problem even if the design calculation is performed by this method.

  Conventionally, critical experiments have been performed for the purpose of quantifying errors in theory and calculation methods and errors due to parameters. However, there has been little detailed physical and mathematical investigation of information obtained from critical experiments so far, and it is useful for experiments that reproduce important features compared to the target system. Experiments have been done based on the judgment that there is.

  The measured values and calculated values of critical experiments have been used for the correction factor (bias) method and cross-sectional area adjustment. Therefore, there has not been provided a method for combining the calculation error information obtained in the critical experiment into the calculation error of the target system. Here, the information is information for checking how much error is included in the numerical value (design expected value) obtained when the target system is calculated with a specific design method and specific parameters. It is.

  Since quantitative and mathematical details have not been examined for the experimental results, it is not possible to determine which items should be emphasized in the experiment in order to clarify the nature of the physical quantity of interest when conducting the experiment. There is a problem that it cannot be determined whether the best experiment has been conducted.

  In addition, the state of the target system cannot always be simulated by experiments over a wide range. For example, if a sufficient amount of material used in the target system cannot be prepared in the experimental facility, the space area in which the material is used becomes part of the experimental system in the experiment. be called.

  There is no known method for evaluating an error of a physical quantity of interest in a target system based on information obtained from a partial simulation experiment.

  Furthermore, there is no known method for quantitatively comparing which experiment is superior when performing a plurality of experiments, or a method for estimating design calculation errors by combining information of a plurality of experiments. In addition, there is currently no effective method for effectively using information on experiments conducted in the past or optimizing resources (human, time, economic resources) necessary for experiments.

  In terms of engineering, when guaranteeing the performance of products and facilities to be manufactured from the design, instead of using the most reliable values obtained from the design as they are, in order to ensure that the design target values are satisfied, in a conservative sense The final design is made by multiplying a certain coefficient so that there is room or adding a constant value. The coefficient at this time is called a safety coefficient. Until now, this safety factor has often been used as a fixed value or determined by the design calculator, and there is little guarantee that the value satisfies both safety and economics in engineering, and it is generally the case. Economic efficiency has been impaired.

  Therefore, the present invention quantitatively analyzes the error included in the simulation result due to the parameter using the experimental result simulating the target system in the simulation using the model expressing the behavior of the object on the computer. It is an object to be able to correct using the estimated correction coefficient.

In order to achieve the above-mentioned object, the present invention is based on a result of estimating an error included in a result of simulation using a model expressing the behavior of a target on a computer using a result of an experiment simulating the target, In the simulation result correction apparatus for correcting the simulation result, when there are n experimental systems, i is an integer satisfying 1 ≦ i ≦ n, and each i-th experiment is obtained by simulation using the model. A comparison value calculation unit and a comparison value for calculating and storing each experimental comparison value CE _i that is a comparison value between the calculated value C _i of the target physical quantity R and the measured value E _i of the physical quantity R measured in each experiment Covariance error matrix calculation for calculating a covariance error matrix W indicating the relationship between the storage unit and the ratio of the uncertainty of the input value to the model used in the simulation When the object system sensitivity coefficient vector calculating unit for calculating a target system sensitivity coefficient vector S _R indicating the amount of change for a unit change in the input values to the model of the result of the simulation using the model for the subject, each i th experimental system sensitivity coefficient and the vector operation unit, the experimental scheme for system of experiments to calculate an experimental system sensitivity coefficient vector S _i representing the variation with respect to unit change in input values to the model of the results of simulation using the model An experimental system sensitivity coefficient vector storage unit that stores the calculation result of the sensitivity coefficient vector calculation unit, and a linear combination constant α _i of a linear combination vector Sk _obtained by linearly combining the experimental system sensitivity coefficient vector _Si , a linear combination constant computing section for obtaining so as to minimize the linear coupling constant storage unit for storing the linear coupling constant alpha _i The covariance error matrix W and the objective system sensitivity coefficient vector _{S R} and RF simulation evaluation factor RF from said linear combination vector ^{_{S k = S R T · W}} · S K / ((S R T · W · S _R ) ^1/2 · (S _K ^T · W · S _K ) ^1/2 ), a simulation property evaluation factor calculation unit, a simulation property evaluation factor storage unit that stores the simulation property evaluation factor RF, and the linear combination A correction index value calculation unit for estimating a correction index value for the physical quantity R of the target obtained as a result of the simulation using the model by combining the values of the respective experimental comparison values with the constant α _i as a weight, and a reliability enhancement A reliability enhancement factor calculation unit that calculates a factor RCF from the value of the simulation evaluation factor RF; and the correction index value calculation unit includes a calculation error correction unit that multiplies the correction index value by the reliability enhancement factor. It is characterized by that.

Further, the present invention automatically uses the simulation result obtained by estimating the error included in the simulation result using the model expressing the behavior of the object on the computer using the result of the experiment simulating the object. in the simulation result correction method for correcting the results, experimental systematic measurement store unit, and storing the measured value Q _E obtained as the result of the experiment, the experimental system measurement store unit, obtained by the simulation system measurement A step of storing a value, a step of deriving and storing a covariance error matrix calculation unit and a covariance error matrix storage unit, and an experimental system physical quantity calculation unit and an experimental system calculation value storage unit The step of performing simulation calculation of the experimental system and storing the calculated value, the comparison value calculation unit and the comparison value storage unit A step of calculating and storing a comparison value between the calculated value and the measured value, a step of calculating and storing the experimental system sensitivity coefficient vector by the computer in the experimental system sensitivity coefficient vector calculation unit and the target system calculation value storage unit, and an actual system A step in which a physical quantity calculation unit and a target system calculation value storage unit perform and store simulation calculation of a target real system, and a target system sensitivity coefficient vector calculation unit and a real system sensitivity coefficient vector storage unit A step of calculating and storing a sensitivity coefficient, a step of calculating a linear combination constant and a linear combination constant storage unit, a step of calculating a linear combination constant, and a simulation evaluation factor calculation unit and a simulation evaluation factor storage unit A step of calculating a simulation evaluation factor RF combining the experimental system, and a step of the correction index value calculation unit calculating the correction index value A step in which the reliability enhancement factor calculation unit and the reliability enhancement factor storage unit calculate the reliability enhancement factor RCF, and a calculation error correction unit calculates the product of the correction index value and the reliability enhancement factor RCF. It is characterized by having.

  According to the present invention, in a simulation using a model in which the behavior of an object is expressed on a computer, the simulation result is determined by quantifying the error included in the simulation result due to the parameter and the proportion of the target system that can be simulated. Since the correction can be made using the reliability enhancement factor estimated from the simulated evaluation factor that has been evaluated, the design calculation value can be optimized and the reliability can be ensured while ensuring the economy.

It is a block diagram which shows the structure of 1st Embodiment of the simulation result correction apparatus which concerns on this invention. It is a conceptual diagram of the square difference SD in the simulation result correction apparatus according to the first embodiment of the present invention. FIG. 6 is a relationship diagram between an angle θ composed of two vectors and the square of the reliability enhancement factor RCF in the simulation result correction apparatus according to the first embodiment of the present invention. It is a graph which shows the relationship between the simulation evaluation factor RF in the simulation result correction | amendment apparatus which concerns on the 1st Embodiment of this invention, square difference ratio SDR, and the reliability enhancement factor RCF. It is a flowchart of the process of 1st Embodiment of the simulation result correction | amendment apparatus which concerns on this invention. It is a block diagram which shows the structure of 2nd Embodiment of the simulation result correction apparatus which concerns on this invention. It is a flowchart of the process of 2nd Embodiment of the simulation result correction | amendment apparatus which concerns on this invention.

  Hereinafter, embodiments of a calculation error processing apparatus according to the present invention will be described with reference to the drawings. Here, the same or similar parts are denoted by common reference numerals, and redundant description is omitted.

[First Embodiment]
FIG. 1 is a block diagram showing a configuration of a first embodiment of a simulation result correcting apparatus according to the present invention. This simulation result correction apparatus 10 can be constructed on a computer (not shown).

  The computer includes a central processing unit (CPU) 100, a storage device 140, an input device 160, and a display device 170. The CPU 100 includes a calculation unit 110 and a control unit 130, and the control unit 130 includes an input control unit 131 and a display control unit 132 as a part thereof.

  Each of these components is connected via a bus 30.

  An input device 160 such as a keyboard or a mouse is connected to the input control unit 131. A display device 170 such as a liquid crystal display is connected to the display control unit 132. Input to the computer, such as an instruction to start calculation to the simulation result correction apparatus 10, is performed via the input device 160. Necessary information such as the estimated error, the reliability enhancement factor, and the result of correcting the simulation result is displayed on the display device 170.

  The calculation unit 110 of the CPU 100 includes a covariance error matrix calculation unit 111, an experimental system physical quantity calculation unit 112, a calculated / measured value relative difference calculation unit 113, a physical quantity relative difference determination unit 114, an experimental system sensitivity coefficient vector calculation unit 115, an actual machine. System physical quantity calculation unit 116, actual system sensitivity coefficient vector calculation unit 117, first linear combination constant calculation unit 118, simulation property evaluation factor calculation unit 119, relative error calculation unit 120, reliability enhancement factor calculation unit 121, and calculation result correction Part 122.

  The storage device 140 includes an experimental system measured value storage unit 141, an experimental system calculated value storage unit 142, an actual system calculated value storage unit 143, a calculated value / measured value relative difference storage unit 144, a relative error storage unit 145, an experimental system. It has a sensitivity coefficient vector storage unit 146, an actual system sensitivity coefficient vector storage unit 147, a linear combination constant storage unit 148, a covariance error matrix storage unit 149, a simulation evaluation factor storage unit 150, and a reliability enhancement factor storage unit 151.

  The simulation result correction apparatus 10 estimates a calculation error associated with a simulation result using a model representing the behavior of the object on a computer, and corrects the simulation result using the estimation result. Here, the system in which the simulation result correction apparatus 10 estimates the calculation error is referred to as a “target system” or an “real machine system”. This objective system is a product such as a nuclear reactor.

  When estimating the calculation error, the simulation result correction apparatus 10 uses the results obtained by n experiments. Here, the experiment is performed by simulating at least a part of the objective system, and the system of the apparatus used for the experiment is called an “experiment system”. In this embodiment, a case where a critical experiment is performed as a simulation experiment will be described.

  In the present embodiment, an error caused by a parameter included in a result of simulation using a model (hereinafter referred to as “model”) expressing the behavior of an object on a computer is quantitatively determined using an experimental result simulating the object. To estimate.

  For this reason, mathematically (or similarity) between the two is first mathematically defined for the experiment and the target actual system. Next, a safety factor is rationally calculated according to the simulation value, and a necessary and sufficient safety factor evaluation method that satisfies both safety and economic aspects is provided.

The experimental system measurement value storage unit 141 stores each measurement value obtained by n experiments. Hereinafter, the measured value obtained in the i-th experiment (i = 1, 2,..., N) for the physical quantity R is denoted as R _E ⁱ .

  The experimental system physical quantity calculation unit 112 performs a simulation for each of the n experimental systems using a model to obtain the value of the physical quantity R.

Hereinafter, the i-th physical quantity R (i = 1,2, ···, n) the calculated value obtained by the simulation of the experiment is expressed as _R ^{C i.} Moreover, the experimental system calculation value storage unit 142 stores the respective calculated values R _C ⁱ obtained by the experiment system physical quantity calculation unit 112.

  The actual machine system physical quantity calculation unit 116 performs simulation using a model for the target actual machine system, and obtains the value of the physical quantity R. The actual machine system operation value storage unit 143 stores the value of the physical quantity R obtained by the actual machine system physical quantity operation unit 116 for the target actual machine system.

により、算出する。 The experimental system sensitivity coefficient vector calculation unit 115 uses the model to simulate the i-th experimental system, and changes the unit value of the input value of the parameter x _i (i = 1, 2,..., M) of the model. An experimental system sensitivity coefficient vector S _i representing the amount of change in R is generated as a physical quantity of _interest .

To calculate.

  As the name suggests, the experimental system sensitivity coefficient vector is a vector quantity, and the number m of parameters is the number of elements.

The experimental system sensitivity coefficient vector storage unit 146 stores the experimental system sensitivity coefficient vector S _i calculated by the experimental system sensitivity coefficient vector calculation unit 115.

により、算出する。 The actual machine system sensitivity coefficient vector calculation unit 117 changes the physical quantity R of interest, which is caused by the unit change of the input value of the parameter x _i (i = 1, 2,..., M) of the model of the simulation result of the target system. the objective system sensitivity coefficient vector S _R representing the amount

To calculate.

  The experimental system sensitivity coefficient vector is also a vector quantity having the number m of parameters as the number of elements.

Actual system sensitivity coefficient vector storage unit 147 stores the experimental system sensitivity coefficient vector S _R calculated in the experimental system sensitivity coefficient vector calculation part 117.

  The covariance error matrix calculation unit 111 calculates a covariance error matrix W indicating the relationship of the ratio of the uncertainty of the input value to the model used for the simulation.

Here, the covariance error matrix is an error matrix that represents the uncertainty of input values of the same structure used in the target system and the experimental system. The covariance error matrix W commonly used in the target system and the experimental system has a value obtained by squaring the relative error of each parameter x _i (i = 1, 2,..., M) on the diagonal component w _ii. The off-diagonal component w _ij contains a product of relative errors between the parameter i and the parameter j.

  The magnitudes of the components of the covariance error matrix W are only required to be correct in relation to each other, and the absolute value is not a problem. It is assumed here that the covariance error matrix that represents the uncertainty about the input of the target system and the covariance error matrix that represents the uncertainty about the input of the system for each experiment are the same matrix. doing.

  In general, the covariance error matrix that represents the proportion of uncertainty of the input values to the model is not necessarily clearly defined. In this case, the covariance error matrix may be a unit matrix.

  The covariance error matrix storage unit 149 stores the covariance error matrix W calculated by the covariance error matrix calculation unit 111.

The first linear combination constant calculation unit 118 calculates the linear combination constant α _i of the linear combination vector S _{k obtained} by linear combination of the experimental system sensitivity coefficient vector S _i , S _k ^T WS _k is equal to S _R ^T WS _R , and determined as the angle between the system sensitivity coefficient vector S _R and a linear combination vector S _k is minimized.

により定義し、算出する。 First, using the sensitivity coefficient vector S _i of the i-th experimental system, the linear combination (linear combination vector) S _k of the sensitivity coefficient vectors satisfying the expression (3) is _obtained .

Defined and calculated.

Here, α _i is an arbitrary constant, which is called a linear combination constant.

Next, the linear coupling constant α _i is
S _k ^T WS _k = S _R ^T WS _R (4)
Satisfied, and determined as object system sensitivity coefficient vector S angle between _R and the linear combination vector S _k theta is minimized.

  Here, T is a symbol indicating transposition of a vector or a matrix. This equation (4) represents a condition that the total amount of relative error is the same.

The method for obtaining such objects systematically sensitivity coefficient vector S angle between _R and the linear combination vector S _k theta is minimized envisioned several.

For example, by minimizing the absolute value of (S _R −S _k ) ^T W (S _R −S _k ), the angle θ formed by the objective system sensitivity coefficient vector S _R and the linear combination vector S _k is minimized. be able to. If the value of ^{_{(S R -S k) T W}} (S R -S k) becomes zero (0), i.e.,
^{_{(S R -S k) T W}} (S R -S k) = 0 ... (5)
If you are satisfied, automatically
S _k ^T WS _R / {(S _k ^T WS _k ) ^1/2 (S _R ^T WS _R ) ^1/2 } = 1 (6)
It becomes. In this case, equation (6) indicates that a simulation evaluation factor RF described later is 1.

Left side of the molecule of formula (6) indicates the inner product of the linear combination vector S _k and purpose systematic sensitivity coefficient vector S _R Considering covariance error matrix W. Left side of the denominator of the equation (6) is adapted to the magnitude of the vector of the linear combination vector S _k and purpose systematic sensitivity coefficient vector S _R Considering covariance error matrix W.

^{_{(S R -S k) T W}} (S R -S k) linear coupling constant alpha _i of the absolute value and the minimum can be determined for example using the undetermined constant method to Lagrange.

d, d = S _k ^T WS _k (7)
Using this formula as a binding condition, Lagrange's undetermined constant λ is used to create the following formula L.

として、α_ｉの条件式（連立方程式）を作成する。この条件式から、λとα_ｉ（ｉ＝１，２，…，ｎ）を決定する。 L = {(S _R −S _k ) ^T W (S _R −S _k )} + λ {S _k ^T WS _k −d}
L = {2d−2 (S _k ^T WS _R )} + λ {S _k ^T WS _k −d} (8)
From this extreme value condition for this equation (8):

As a result, a conditional expression (simultaneous equations) of α _i is created. From this conditional expression, λ and α _i (i = 1, 2,..., N) are determined.

Now, _assuming that r _ij = S _i ^T WS _j and r _Ri = S _i ^T WS _j , an equation for solving λ and α _i (i = 1, 2,..., N) using Lagrange undetermined constant λ. Can be written as:

R · λ · α = r (10)
Here, R is a matrix composed of R _ij , α is a column vector composed of α _i , and r is a column vector composed of r _Ri .

となる。この式を解いて、λα_ｉ（ｉ＝１，２，…，ｎ）を求める。 As an example, a case where R is a 4 × 4 matrix will be described. In this case, the above equation is

It becomes. By solving this equation, λα _i (i = 1, 2,..., N) is obtained.

により、λα_ｉ（ｉ＝１，２，…，ｎ）を算出する。 That is,

To calculate λα _i (i = 1, 2,..., N).

が成り立つように規格化される。 In addition, α _i is

Is standardized so that

In this way, λ and α _i (i = 1, 2,..., N) are determined.

Note that λ and α _i (i = 1, 2,..., N) are given as positive and negative. In principle, the code with the smaller 2d−2 · (S _k ^T WS _R ) is selected. However, since it is known that the newly defined simulation evaluation factor is equal to λ, and the simulation evaluation factor should be a value close to 1, it is not appropriate to set λ to a negative value. Therefore, a positive value should always be selected as λ.

The linear combination constant storage unit 148 stores the linear combination constant α _i (i = 1, 2,..., N) calculated by the first linear combination constant calculation unit 118.

  The simulation evaluation factor calculation unit 119 mathematically defines the similarity between the experimental system and the target actual machine system.

Sensitivity coefficient related to the physical parameters R of interest with respect to the experimental system vector S _K, using the physical parameters R about sensitivity coefficient vector S _R of interest with respect to actual systems for the _purpose, and the covariance error matrix W of the parameters used in the calculation, these It is possible to obtain a simulation evaluation factor (Representative Factor: RF) indicating the similarity between the two systems.

The simulation evaluation factor RF is
_{^{RF = S R T WS K /}} ((S R T WS R) 1/2 (S K T WS K) 1/2) ... (14)
Calculated by

And the simulated evaluation factor RF is when the angle between the object system sensitivity coefficient vector S _R and a linear combination vector S _k was theta, it represents cos [theta]. If this value is 1, i.e., when cosθ = 1, θ = 0, and the linear combination vector S _k and purpose systematic sensitivity coefficient vector S _R and the overlapped state, i.e., are mathematically perfect match It will be. Conversely, when there is no relationship, the simulation evaluation factor is zero.

When the value of the evaluation value for minimizing the absolute value of the linear combination constant α _i is zero (0), that is,
^{_{(S R -S k) T W}} (S R -S k) = 0 ... (15)
If you are satisfied, automatically,
S _k ^T WS _R / {(S _k ^T WS _k ) ^1/2 (S _R ^T WS _R ) ^1/2 } = 1 (16)
It becomes.

This equation (16) shows that the simulation evaluation factor RF is 1. (16) The left-hand side of the molecule of formula indicates the inner product of the linear combination vector S _k and purpose systematic sensitivity coefficient vector S _R Considering covariance error matrix W. (16) The left-hand side of the denominator of formula is adapted to the magnitude of the vector of the linear combining vector S _k and purpose systematic sensitivity coefficient vector S _R Considering covariance error matrix W.

  The simulation evaluation factor storage unit 150 stores the simulation evaluation factor RF calculated by the simulation evaluation factor calculation unit 119.

  The reliability enhancement factor calculation unit 121 obtains a safety coefficient using the simulation evaluation factor RF calculated by the simulation evaluation factor calculation unit 119. Of course, the safety factor is defined according to a clear mathematical meaning. Hereinafter, the safety factor is referred to as a reliability enhancement factor RCF.

  Described below is the derivation of the reliability enhancement factor RCF.

In general, S _K and S _R do not completely match, and there is a difference (vector), and the engineer is next interested in the magnitude of the vector of the difference.

Already been performed normalized to the square of the magnitude with respect to S _K d, the magnitude of the square of the vector difference S _K and S _R if covariance error matrix W, i.e. squared difference SD here Can be defined in the following quadratic form of vectors and matrices.

^{_{SD = (S R -S K)}} T W (S R -S K)
= S _R ^T WS _R + S _K ^T WS _K -2 S _K ^T WS _R
= D + d-2S _R ^T WS _K (17)
This equation can be transformed as follows. This deformation is established as the second cosine theorem in the triangle formed by the two vectors S _K and S _R.

FIG. 2 is a conceptual diagram of the square difference SD in the simulation result correction apparatus according to the first embodiment of the present invention. Figure 2 shows an example of some of the angle formed this and two vector S _K and S _R is.

In this case, the simulated evaluation factor RF is a cosine of the angle formed two vector S _K and S _R in the case where the covariance error matrix W is the identity matrix.

  Further, the equation (17) can be modified as follows.

^{_{SD = d + d-2S R}} T WS K = 2d-2dS R T WS K / d
= 2d-2dS _R ^T WS _K / ((√d) (√d))
^{_{= 2d-2dS R T WS K}} / ((S R T WS R) 1/2 (S K T WS K) 1/2)
= 2d-2d · RF = 2d (1-RF) (18)
Since the square difference SD can be defined, it is possible to define the squared difference SD of two vectors S _K and the vector S _R, the squared difference ratio SDR for the d by the following equation.

SDR = SD / d = 2 (1-RF) where 0 ≦ RF ≦ 1 (19)
If the above equation (18) is evaluated under the condition of the equation (19), the range of SDR values can be easily evaluated,
0 ≦ SDR ≦ 2 (20)
It becomes.

The square difference ratio SDR indicates how many times the calculation error S _K ^T WS _K (= d) found in the experimental system corresponds to the magnitude of the difference vector between the two vectors S _K and S _R. It is a numerical value.

  As described above, the SDRs of the above formulas (18) and (19) indicate the remaining calculation error that cannot be evaluated when the calculation error related to the target actual machine system (related to the physical parameter of interest) is obtained. It can be said that it shows the proportion of size.

It seems difficult to understand that the value of the squared difference ratio SDR is a maximum of 2, but if the two sensitivity coefficients (vectors) S _K and S _R have no similarity at all and are in an orthogonal relationship, When the simulation evaluation factor is 0, the square difference ratio SDR becomes 2 which is the maximum value.

  However, this is a special case, or a case where the simulation evaluation factor that originally has no similarity is 0 and is not suitable for evaluation of calculation error. It has become.

  Except for such extreme cases, it can be generally considered that the simulated valence factor RF is larger than 0.7. This is because the engineer originally intends to adopt a (critical) experiment that is as similar as possible to the target real machine system. Therefore, the SDR is generally smaller than 0.6. Further, if the mockness evaluation factor RF is larger than 0.9, the SDR is smaller than 0.2, and these relationships can be said to be appropriate (see FIG. 2 and FIG. 4 described later).

  Next, a reliability enhancement factor RCF is newly defined using the previously obtained square difference ratio SDR and the simulation evaluation factor RF stored in the simulation evaluation factor storage unit 150.

  As described above, even if a calculation error related to the target actual machine system (related to the physical parameter of interest) is obtained, a calculation error (uncertainty) that cannot be evaluated remains because the simulation value factor RF is not always 1.0. Because.

  FIG. 3 is a relationship diagram between the angle θ formed by two vectors and the square of the reliability enhancement factor RCF in the simulation result correction apparatus according to the present embodiment.

When the calculation error related to the parameter used in the calculation is corrected for the calculated value of the target actual system,
a) The square of the relative error to be corrected is set to d. b) the square of the magnitude of the vector difference _{S K} and _{S R,} i.e. squared difference SD here, it is SD = 2d · (1-RF ) simulation of valence factor as RF. c) As a correction coefficient, first consider the sum of d and SD, and obtain a value obtained by dividing the sum by d. d) Define the square root of the value as a new correction factor (reliability enhancement factor RCF). This is based on the idea.

RCF = [(d + SD) / d] ^1/2
= (1 + SD / d) ^1/2
= (1 + SDR) ^1/2
= [1 + 2 · (1-RF)] ^1/2
= (3-2 · RF) ^1/2 (21)
The above processing will be described with reference to FIG. First, as described above, the magnitude of the square of the vector difference S _K and S _R, i.e. obtaining the squared difference SD here. Next, this absolute value is equal, and consider a vector SDa orthogonal to the S _R. Absolute value of the vector obtained by synthesizing the vector S _R and vector SDa becomes the square of the reliability enhancement factor RCF seeking.

  FIG. 4 is a graph showing the relationship between the simulation evaluation factor RF, the square difference ratio SDR, and the reliability enhancement factor RCF in the simulation result correction apparatus according to the first embodiment of the present invention. As shown in FIG. 4, SDR takes a value in the range of 0 to 2, but RCF takes a value in the range of 1 to √3, and is a form suitable for a coefficient for correction.

Where RCF is
RCF = (3-2RF) ^1/2 ≈2-RF (22)
Can be approximated by

  The last term on the right side of this equation is a good approximation for RF ≧ 0.7.

For example, when RF = 0.7, the original equation is (3-2RF) ^1/2 = 1.265, whereas the approximate equation is 2-RF = 1.300, which is a difference of about 3%. Yes, it is a good approximation because of the difference in relative error levels.

  The reliability enhancement factor storage unit 151 stores the reliability enhancement factor RCF calculated by the reliability enhancement factor calculation unit 121.

The calculated value / measured value relative difference calculation unit 113 calculates an error included in the simulation result using the model as a relative difference E _r ⁱ (i = 1, 2,..., N) regarding each experiment.

  Here, i is a natural number from 1 to the total number n of experiments.

That is, the relative difference E _r ⁱ is an error of the calculated value of the physical quantity R obtained by the model for the i-th experiment with respect to the measured value of the physical quantity R measured in the experiment.

Here, the relative difference E _r ⁱ (i = 1, 2,..., N) has the following meaning.

Now, let the measured value obtained for the physical quantity R of interest in a simulation experiment such as a critical experiment be R _E, and let the calculated value obtained when a simulation calculation is performed using a certain model be R _C.

The relative difference E _P ⁱ between the calculated value R _C and the measured value R _E is:
^{_{E r i = (R C -R}} E) / R E = (R C / R E) -1 ... (23)
Is calculated by

This value is
It is almost equal to “relative error due to parameters” + “relative error due to calculation method” – “measurement error”. The measurement error here is a relative error.

From this value, “relative error due to calculation method” and “measurement error” are removed. “Relative error due to calculation method” and “measurement error” are usually not positive or negative.
{(R _C / R _E ) −1} ² − “Relative error by calculation method” ² − “Relative measurement error” ² is the square of the relative difference E _P ⁱ caused by the parameters in the experimental system, that is, (E _P ⁱ ) Consider ² This calculation is not an accurate evaluation, but is an evaluation for convenience. When the relative error by the calculation method is not clear, it may be set to 0.

Further, when “relative error by calculation method” is sufficiently small, (E _P ⁱ ) ² is obtained from (E _r ⁱ ) ^2- “relative measurement error” ² , or when “relative measurement error” is sufficiently small ( When E _P ⁱ ) ² is obtained from (E _r ⁱ ) ^2- "Relative error by calculation method" ² and both "Relative error by calculation method" and "Relative measurement error" are sufficiently small, (E _r ⁱ ) ² And (E _P ⁱ ) ² may be used. As for whether or not it is sufficiently small, for example, the square of “relative measurement error” or the square of “relative error by calculation method” is about 1/100 with respect to {(R _C / R _E ) −1} ² The judgment value may be used as a reference.

The physical quantity relative difference determination unit 114 gives the absolute value of the relative difference E _r ⁱ (i = 1, 2,..., N) calculated by the calculated value / measured value relative difference calculation unit 113 in advance for determination. It is determined whether or not it is within the n specified values, and a determination value as to which one is output.

  These steps are repeated for a total of n experiments.

The calculated value / measured value relative difference storage unit 144 stores the relative difference E _r ⁱ calculated by the calculated value / measured value relative difference calculation unit 113 and (E _P ⁱ ) ² (i = 1, 2,..., N). And remember.

Evaluation is made and the number of experiments is combined, and the calculation error (uncertainty) (ΔZ / Z) ² of the target actual machine system is evaluated by the following method.

The relative error calculation unit 120 uses the linear combination constant α _i stored in the linear combination constant storage unit 148 as a weight and synthesizes the value of the relative difference E _Pi to obtain the error ΔZ of the target physical quantity Z of the target system. On the other hand, the square (ΔZ / Z) ² of the relative error caused by the parameter used for the calculation is obtained.

（ただし、
ＣＯＲ_iｊ＝Ｓ_ｉ ^ＴＷＳ_ｊ／｛（Ｓ_ｉ ^ＴＷＳ_ｉ）^１／２（Ｓ_ｊ ^ＴＷＳ_ｊ）^１／２｝）
により算出される。 Specifically, the square of this relative error (ΔZ / Z) ² is

(However,
COR _ij = S _i ^T WS _j / {(S _i ^T WS _i ) ^1/2 (S _j ^T WS _j ) ^1/2 })
Is calculated by

At this time, the sign of the linear combination constant α _i stored in the linear combination constant storage unit 148 is considered.

  The calculation result correcting unit 122 calculates the calculation error (uncertainty) calculated by the calculated value / measured value relative difference storage unit 144, that is, the correction amount represented by (ΔZ / Z) that can be obtained from the equation (24). On the other hand, by multiplying the reliability enhancement factor RCF calculated by the reliability enhancement factor calculation unit 121, an error considering the safety coefficient, that is, the reliability enhancement factor RCF, is applied to the calculated value of the target actual machine system.

  In other words, if this correction factor (reliability enhancement factor RCF) is further multiplied by a value that corrects the calculation error related to the parameter used in the calculation for the calculated value of the actual system, the correction of the calculated value is theoretically conservative. Furthermore, rational correction based on RF can be performed on the simulated valence factor.

  The good point of this correction factor is that this factor was derived according to mathematical considerations. Another good point is that this correction factor has a direct relationship with the simulation evaluation factor. If the sex evaluation factor is calculated, it can be easily calculated.

Different covariance (error) matrices may be used for the target system and the experimental system. _Let W _{R be} the covariance matrix of the target system, and W _E be the covariance matrix of the experimental system. However, the structure of W _R and W _E is the same, each other (i, j) in this case two of the matrix components is assumed to be defined in the same units. That is, it is assumed that the magnitudes of the (i, j) components can be directly compared.

In this case, the equations (4) and (5) are
S _k ^T W _R S _k = S _R ^T W _R S _R (25)
and,
(S _R −S _k ) ^T W _R (S _R −S _k ) = 0 (26)
It becomes. Using these equations, the linear coupling constant α _i is obtained.

Further, constants t _i (i = 1, 2,..., N) are determined so that the following equation holds.

（ただし、
ＣＯＲ_iｊ＝Ｓ_ｉ ^ＴＷＳ_ｊ／｛（Ｓ_ｉ ^ＴＷＳ_ｉ）^１／２（Ｓ_ｊ ^ＴＷＳ_ｊ）^１／２｝）
により求められる。 ^{_{t i S i T W E S}} i = S i T W R S i ... (27)
The constant t _i (i = 1, 2,..., N) may be determined by the first linear combination constant calculation unit 118, for example. By using the thus constant t _i obtained, the square of the relative error of the system of interest,

(However,
COR _ij = S _i ^T WS _j / {(S _i ^T WS _i ) ^1/2 (S _j ^T WS _j ) ^1/2 })
Is required.

  FIG. 5 is a flowchart of processing according to the present embodiment.

  First, when starting the simulation calculation, n prescribed values for determination are set (S01).

  Next, a target actual machine system is selected (S02). In addition, n types of simulation experiment systems are selected (S03).

  The experimental system measured value storage unit 141 stores each measured value of the simulated experimental system (S04).

  Further, a method (simulation method) for calculating the physical quantity of interest is selected (S05), and parameter input for calculation is selected and set (S06).

  Next, the relative error by the calculation method resulting from the simulation method selected in step S05 and the relative measurement error in the experimental system are calculated and stored (S07). This calculation is performed by the covariance error matrix calculation unit 111. The experimental system calculation value storage unit 142 stores the relative error and the calculation result of the relative measurement error in the experimental system.

  Next, the covariance error matrix calculation unit 111 derives a covariance error matrix, and the covariance error matrix storage unit 149 stores the result (S08).

  Next, the experimental system physical quantity computing unit 112 performs a simulation calculation related to the simulated experimental system based on the calculation method selected in step S05 and the parameters set in step S06. The obtained calculation result is stored and stored in the experimental system calculation value storage unit 142 (S09).

Next, the calculated value / measured value relative difference calculating unit 113 measures the measured value of the i-th simulated experimental system stored in the experimental system measured value storage unit 141 in step S04, and the experimental system calculated value storage unit 142 in step S09. The relative error E _r ⁱ is calculated based on the calculation result for the i-th simulation experiment system stored in the _R , and the relative error by the calculation method stored in the experiment system operation value storage unit 142 in step S07, and the experiment system (E _P ⁱ ) ² is calculated using the relative measurement error at. The calculated value / measured value relative difference storage unit 144 stores and stores the result (S10).

Next, the physical quantity relative difference determination unit 114 determines whether or not the result of step S10, that is, the absolute value of the E _r ⁱ value is equal to or less than the i-th specified value (S11).

When the absolute value of E _r ⁱ exceeds the i-th specified value, the method used for the simulation is reviewed (S05).

  The above steps S04 to S11 are repeated n times, which is the number of simulation experiment systems (S12).

  Next, the experimental system sensitivity coefficient vector calculation unit 115 derives the sensitivity coefficient of the simulation experiment based on the calculation method selected in step S05 and the parameters input in step S06. The experimental system sensitivity coefficient vector storage unit 146 stores this result (S13).

  Next, the actual machine system physical quantity calculation unit 116 executes a simulation calculation related to the target actual machine system. The actual machine system operation value storage unit 143 stores and stores the calculation results obtained for the actual machine system (S14).

  Thereafter, the actual machine system sensitivity coefficient vector calculation unit 117 derives the sensitivity coefficient of the physical quantity in the target system. The actual machine system sensitivity coefficient vector storage unit 147 stores the result (S15).

The first linear combination constant calculation unit 118 minimizes the absolute value of (S _R −S _k ) ^T · W · (S _R −S _k ), thereby reducing the objective system sensitivity coefficient vector S _R and the linearity. A linear coupling constant α _i that minimizes the angle formed with the coupling vector S _k is obtained (S16).

The simulation property evaluation factor calculation unit 119 is linear with the covariance error matrix stored in the covariance error matrix storage unit 149 in step S08 and the simulation experiment sensitivity coefficient stored in the experimental system sensitivity coefficient vector storage unit 146 in step S13. Based on the linear coupling coefficient linear coupling constant α _i stored in the coupling constant storage unit 148 and the sensitivity coefficient in the actual machine system stored in the actual machine system sensitivity coefficient vector storage unit 147 in step S15, the simulation property combining the simulated experiment system An evaluation factor RF is calculated. The simulation evaluation factor storage unit 150 stores the calculated simulation evaluation factor RF (S17).

Next, the relative error calculation unit 120 uses the coefficient obtained in the process of calculating the simulation evaluation factor RF to calculate the E _r ⁱ value (i = 1, 2,..., N) and its sign. The absolute value and sign of the calculation error RD of the target actual machine system are calculated. The relative error storage unit 145 stores this result (S18).

  Next, the reliability enhancement factor calculation unit 121 calculates the reliability enhancement factor RCF based on the calculated value of RF, and the reliability enhancement factor storage unit stores this (S19).

  Finally, the calculation result correction unit 122 performs a correction operation by combining the calculation error RD and the reliability enhancement factor RCF (S20), and ends the above automatic process.

  In this way, the + covariance (error) matrix that represents the uncertainty about the input of the target system and the covariance (error) matrix that represents the uncertainty about the input of the system for each experiment are called the same matrix Without making assumptions, even if this covariance error matrix is different, the formula to be processed can be generalized to estimate the calculation error.

Finally, the safety factor (reliability enhancement factor) RCF from the simulation evaluation factor RF obtained so far is:
RCF = (3-2RF) ^1/2 ≈2-RF (22)
Is required. When RF is greater than 0.7, the rightmost expression in the above expression is a very good approximation.

(effect)
According to the present invention, in a simulation using a model, an error included in a simulation result due to a parameter is corrected using a correction coefficient quantitatively estimated using an experimental result simulating a target system. be able to.

[Second Embodiment]
FIG. 6 is a block diagram showing the configuration of the second embodiment of the simulation result correcting apparatus according to the present invention. The configuration of the entire computer is the same as that of the first embodiment. In the present embodiment, some of the functional components of the calculation unit 110 and the storage device 140 are different.

  The calculation unit 110 of the CPU 100 includes a covariance error matrix calculation unit 111, an experimental system physical quantity calculation unit 112, a calculated value / measured value relative ratio calculation unit 125, an experimental system sensitivity coefficient vector calculation unit 115, an actual system physical quantity calculation unit 116, an actual machine. The system sensitivity coefficient vector calculation unit 117, the second linear combination constant calculation unit 126, the simulation evaluation factor calculation unit 119, the correction factor BF calculation unit 127, the reliability enhancement factor calculation unit 121, and the calculation result correction unit 122 are included.

  That is, the calculated value / measured value relative ratio calculation unit 125, the second linear combination constant calculation unit 126, and the correction factor BF calculation unit 127 are different.

  The storage device 140 includes an experimental system measured value storage unit 141, an experimental system calculated value storage unit 142, an actual system calculated value storage unit 143, a calculated value / measured value relative ratio storage unit 155, a correction factor BF storage unit 156, an experimental system sensitivity. It has a coefficient vector storage unit 146, a real machine system sensitivity coefficient vector storage unit 147, a linear combination constant storage unit 148, a covariance error matrix storage unit 149, a simulation evaluation factor storage unit 150, and a reliability enhancement factor storage unit 151.

  That is, the calculated value / measured value relative ratio storage unit 155 and the correction factor BF storage unit 156 are different.

  As described above, in the first embodiment, the measured value in the experimental system and the calculated value in the experimental system are compared based on the difference between them, but in the present embodiment, the comparison is performed based on the ratio between the two. Is.

  When the number of (critical) experiments performed is 1, the correction factor (bias) method is often applied. The correction factor (bias) method aims to reduce errors caused by calculation. For example, it is assumed that a design calculation of a target system is performed using a specific calculation method (computer program) and a specific nuclear data library (parameters used in the calculation). Focusing on a specific physical quantity, it is assumed that the relative error rate of the calculated value obtained in the critical experiment is the same as the relative error rate of the calculated value of the target system.

  Next, a correction factor (bias) is calculated from the calculated value and the measured value of the critical experiment. By multiplying the calculated value of the target system by this correction factor, the relative error of the calculated value of the target system can be removed, and the calculation accuracy of the target system can be improved.

The true value of the critical experiment is T _E , the calculated value in the critical experiment is C _E = T _E (1 + Δc _e ), and the measured value in the critical experiment is E _E = T _E (1 + Δ _e ). Here, Δc _e is a calculation error in a critical experiment, and Δe is a measurement error. When the ratio of the measured value R _{E in} the experimental system to the calculated value _{CE in} the experimental system is represented by CR,
Since CR = E _E / C _E = T _E (1 + Δe) / T _E (1 + Δc _e ) = (1 + Δe) / (1 + Δc _e ), the calculated value C _d = T _d (1 + Δc _d ) intended for this CR In general, since the experimental measurement error Δe is smaller than the calculation error Δc _e , if Δc _d and Δc _e are approximately equal,
R _d = C _d × CR
= T _d (1 + Δc _d ) × (1 + Δe) / (1 + Δc _e )
≒ T _d (1 + Δe + Δc _d −Δc _e )
≈T _d (1 + Δe) (29)
Next, it is possible to estimate the value with reduced error due calculated from the calculated values R _d of the system of interest. This is the correction factor (bias) method. Incidentally, T _d is the true value, .DELTA.c _d is a calculation error included in the calculated value of interest.

The calculated value / measured value relative ratio calculation unit 125 is a ratio of two values of the measured value E _i of the i-th simulation system and the calculated value C _i obtained by using the calculation method (simulation method) of the simulation system. CR _i ,
CR _i = E _i / C _i (i = 1, 2,..., N) (30)
Calculated by

Therefore, CR _i exists in the number n of the simulated experiment system.

The calculated value / measured value relative ratio storage unit 155 stores CR _i (i = 1, 2,..., N) calculated by the calculated value / measured value relative ratio calculation unit 125.

から求める。 The second linear combination constant calculation unit 126 calculates a linear combination constant α _i of a linear combination vector Sk _obtained by linearly combining the experimental system sensitivity coefficient vector S _i ,

Ask from.

  Here, ΔM represents the uncertainty of the calculation method, ΔE represents the measurement error, and V represents the variance thereof.

あるいはｃを１として、

である。 Here, a conditional expression is imposed on the linear combination coefficient α _i . That is,
All α _i ≧ 0 (positive or zero) (32)
and

Or c is 1,

It is.

Usually, unless there is a special meaning, the sum of the linear combination coefficients α _i is normalized to 1 (see Non-Patent Document 1).

How to minimize I of formula (31), Monte also may by numerical calculations such Carlo method, a conditional expression at zero it by partially differentiating I with alpha _i alpha _i Equation (31) It suffices to solve within the range where the expression (33) holds.

The linear combination constant storage unit 148 stores the linear combination constant α _i (i = 1, 2,..., N) calculated by the second linear combination constant calculation unit 126.

により算出する。 The correction factor BF calculation unit 127 stores CR _i (i = 1, 2,..., N) stored in the calculated value / measured value relative ratio storage unit 155 in the second linear combination constant calculation unit 126. Is linearly coupled using the linear coupling constant α _i , and the correction factor BF is

Calculated by

によりＬＢＦを算出する。 A value obtained by taking the natural logarithm of E _i and C _i and linearly combining the difference between the two by weighting the linear combination factor α _i is defined as LBF. That is,

To calculate the LBF.

あるいはｃを１とした

である。 Again, a conditional expression is imposed on the linear combination coefficient α _i . That is,
All α _i ≧ 0 (positive or zero)
and

Or c is 1

It is.

Further, a value obtained by multiplying the natural logarithm e to the LBF power is set as a correction factor BF. That is,
BF = exp (LBF) (39)
This method is shown in Non-Patent Document 2 on page 1513 as the PE method.

  The correction factor BF storage unit 156 stores the correction factor BF calculated by the correction factor BF calculation unit 127.

The correction factor (bias) technique aims at reducing the relative error caused by calculation in actual system of interest, but will be multiplied by the correction factor BF to C _d, this time, the (critical) experiment Purpose If the simulation value factor RF with the actual machine system to be calculated is calculated, the correction coefficient (reliability enhancement factor RCF) can be easily obtained. If this value is further multiplied to obtain C _d × BF × RCF, it is reasonable. Therefore, reliability can be increased.

  FIG. 7 is a flowchart of the processing of this embodiment.

  First, a target actual machine system is selected (S02). In addition, n types of simulation experiment systems are selected (S03).

  The experimental system measured value storage unit 141 stores each measured value of the simulated experimental system (S04).

  Further, a method (simulation method) for calculating the physical quantity of interest is selected (S05), and parameter input for calculation is selected and set (S06).

  Next, the covariance error matrix calculation unit 111 derives a covariance error matrix, and the covariance error matrix storage unit 149 stores the result (S08).

  Next, the experimental system physical quantity computing unit 112 performs a simulation calculation related to the simulated experimental system based on the calculation method selected in step S05 and the parameters set in step S06. The obtained calculation result is stored and stored in the experimental system calculation value storage unit 142 (S09).

Next, the calculated value / measured value relative ratio calculating unit 125 calculates the measured value of the i-th simulated experimental system stored in the experimental system measured value storage unit 141 in step S04, and the experimental system calculated value storage unit 142 in step S09. The value of the relative ratio difference CR _i is calculated based on the calculation result for the i-th simulation experiment system stored in step S1, and the calculated value / measured value relative ratio storage unit 155 stores and stores this result (S10).

  The above steps S04 to S10 are repeated n times, which is the number of simulation experiment systems (S12).

  Next, the experimental system sensitivity coefficient vector calculation unit 115 derives the sensitivity coefficient of the simulation experiment based on the calculation method selected in step S05 and the parameters input in step S06. The experimental system sensitivity coefficient vector storage unit 146 stores this result (S13).

  Next, the actual machine system physical quantity calculation unit 116 executes a simulation calculation related to the target actual machine system. The actual machine system operation value storage unit 143 stores and stores the calculation results obtained for the actual machine system (S14).

  Thereafter, the actual machine system sensitivity coefficient vector calculation unit 117 derives the sensitivity coefficient of the physical quantity in the target system. The actual machine system sensitivity coefficient vector storage unit 147 stores the result (S15).

The second linear coupling constant calculator 126 obtains a linear coupling constant α _i that minimizes I in the equation (31). The linear combination constant storage unit 148 stores this result (S30).

The simulation property evaluation factor calculation unit 119 is linear with the covariance error matrix stored in the covariance error matrix storage unit 149 in step S08 and the simulation experiment sensitivity coefficient stored in the experimental system sensitivity coefficient vector storage unit 146 in step S13. Based on the linear coupling coefficient linear coupling constant α _i stored in the coupling constant storage unit 148 and the sensitivity coefficient in the actual machine system stored in the actual machine system sensitivity coefficient vector storage unit 147 in step S15, the simulation property combining the simulated experiment system An evaluation factor RF is calculated. The simulation evaluation factor storage unit 150 stores the calculated simulation evaluation factor RF (S17).

Next, the correction factor BF calculation unit 127 uses the CR _i value (i = 1, 2,..., N) using the coefficient obtained in the process of calculating the simulation evaluation factor RF. The correction factor BF of the actual machine system is calculated. The correction factor BF storage unit 156 stores this result (S31).

  Next, the reliability enhancement factor calculation unit 121 calculates a safety coefficient (reliability enhancement factor) RCF from the calculated value of RF (S19).

  Finally, the calculation result correction unit 122 performs a correction operation by combining the correction factor BF and the reliability enhancement factor RCF (S32), and ends the above automatic process.

  In principle, the covariance error matrix is defined for all the inputs used in the simulation, but if the evaluation is very difficult, the covariance error matrix may be used as a unit matrix.

(effect)
According to the present embodiment, in a simulation using a model, an error included in a simulation result due to a parameter is corrected using a correction coefficient that is quantitatively estimated using an experimental result simulating a target system. can do.

[Other Embodiments]
As mentioned above, although some embodiment of this invention was described, these embodiment is shown as an example and is not intending limiting the range of invention.

  For example, a reactor has been described as an example of a target system, and a system of a critical experiment apparatus that simulates a nuclear reactor as an experimental system.

Further, for example, in the first embodiment, as a method of obtaining the linear combination constant α _i , the case of the typical Lagrange's undetermined constant method has been shown, but other methods that obtain the same result may be used.

  Furthermore, you may combine the characteristic of each embodiment. In addition, these embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention.

  For example, in each embodiment, when calculating and storing, each calculation function part called a calculation part and each storage function part in a storage device called a storage part are described in a divided form. However, when the calculated result is used in the next step, the step is usually performed by temporarily placing it in the CPU without storing it in the storage device. Therefore, the case where the storage unit described as being in the storage device is temporarily stored as a part of the calculation part in the CPU is also included in the embodiment of the present invention.

  These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 10 ... Simulation result correction apparatus 30 ... Bus 100 ... Central processing unit (CPU)
DESCRIPTION OF SYMBOLS 110 ... Calculation part 111 ... Covariance error matrix calculation part 112 ... Experimental system physical quantity calculation part 113 ... Calculated value / measured value relative difference calculation part (comparison value calculation part)
114 ... Physical quantity relative difference determination unit 115 ... Experimental system sensitivity coefficient vector calculation unit 116 ... Real machine system physical quantity calculation unit 117 ... Real machine system sensitivity coefficient vector calculation unit 118 ... First linear combination constant Calculation unit 119... Simulation property evaluation factor calculation unit 120... Relative error calculation unit (correction index value calculation unit)
121 ... Reliability enhancement factor calculation unit 122 ... Calculation result correction unit 125 ... Calculation value / measured value relative ratio calculation unit (comparison value calculation unit)
126 ... second linear combination constant calculation unit 127 ... correction factor BF calculation unit (correction index value calculation unit)
DESCRIPTION OF SYMBOLS 130 ... Control part 131 ... Input control part 132 ... Display control part 140 ... Memory | storage device 141 ... Experimental system measured value storage part 142 ... Experimental system calculation value storage part 143 ... Actual system calculation value storage unit 144... Calculated value / measured value relative difference storage unit (comparison value storage unit)
145 ... Relative error storage unit (correction index value storage unit)
146 ... Experimental system sensitivity coefficient vector storage unit 147 ... Actual system sensitivity coefficient vector storage unit (target system sensitivity coefficient vector storage unit)
148: Linear combination constant storage unit 149: Covariance error matrix storage unit 150 ... Simulation evaluation factor storage unit 151 ... Reliability enhancement factor storage unit 155 ... Calculated value / measured value relative ratio Storage unit (comparison value storage unit)
156... Correction factor BF storage unit (correction index value storage unit)
160 ... input device 170 ... display device

Claims (10)

In the simulation result correcting apparatus for correcting the simulation result based on the result of estimating the error included in the result of the simulation using the model expressing the behavior of the target on the computer, using the result of the experiment simulating the target,
When there are n experimental systems, i is an integer satisfying 1 ≦ i ≦ n, and the calculated value C _i of the target physical quantity R obtained by the simulation using the model for each i-th experiment, A comparison value calculation unit and a comparison value storage unit for calculating and storing each experimental comparison value CE _i which is a comparison value with the measured value E _i of the physical quantity R measured in each experiment;
A covariance error matrix calculation unit for calculating a covariance error matrix W indicating the relationship of the proportion of uncertainty of input values to the model used for simulation;
And purpose system sensitivity coefficient vector calculating unit for calculating a target system sensitivity coefficient vector S _R indicating the amount of change for a unit change in the input value to the result the model of the simulation using the model for the subject,
An experimental system sensitivity coefficient vector computing unit that computes an experimental system sensitivity coefficient vector S _i representing the amount of change with respect to the unit change of the input value to the model as a result of simulation using the model for each i-th experimental system;
An experimental system sensitivity coefficient vector storage unit for storing a calculation result of the experimental system sensitivity coefficient vector calculation unit;
A linear combination constant computing unit for obtaining a linear combination constant α _i of a linear combination vector S _{k obtained} by linearly combining the experimental system sensitivity coefficient vector S _i so that a predetermined evaluation value is minimized;
A linear combination constant storage unit for storing the linear combination constant α _i ;
RF simulated evaluation factor RF from said covariance error matrix W and the objective system sensitivity coefficient vector S _R and the linear combination vector S _{k
^{= S R T · W · S}} K / ((S R T · W · S R) 1/2 · (S K T · W · S K) 1/2)
A mockness evaluation factor calculation unit obtained by
A simulation evaluation factor storage unit for storing the simulation evaluation factor RF;
A correction index value calculator that estimates a correction index value for the physical quantity R of the target obtained as a result of simulation using the model by combining the values of the experimental comparison values with the linear combination constant α _i as a weight;
A reliability enhancement factor calculation unit for calculating a reliability enhancement factor RCF from the value of the simulation evaluation factor RF;
A calculation error correction unit for multiplying the correction index value by the reliability enhancement factor;
A simulation result correction apparatus comprising: The comparison value calculation unit calculates a relative difference E _r ⁱ = (C _i −E _i ) / E _i (i = 1, 2,..., N) as the comparison value and then calculates in advance. Based on the relative error E ₁ ⁱ and the relative measurement error E ₂ ⁱ by the method,
(E _P ⁱ ) ² = (E _r ⁱ ) ^2- (E ₁ ⁱ ) ^2- (E ₂ ⁱ ) ²
The simulation result correction apparatus according to claim 1, wherein (E _P ⁱ ) ² is obtained by the following. The simulation according to claim 1, wherein the comparison value calculation unit calculates a relative ratio CR _i = E _i / C _i (i = 1, 2,..., N) as the comparison value. Result correction device. The linear combination constant calculation unit minimizes the absolute value of (S _R −S _k ) ^T · W · (S _R −S _k ), thereby reducing the objective system sensitivity coefficient vector S _R and the linear combination vector S _Find the linear coupling constant α _i that minimizes the angle formed by _k ,
The correction index value calculator calculates the square RD ² of the relative error RD calculated as the correction index value.

The RD code is S _i ^T · W · S _R / {(S _i ^T · W · S _i ) ^1/2 (S _R ^T · W · S _R ) ^1/2 }
Is obtained as a sign obtained by multiplying the sign of this value by i (i = 1, 2,..., N) where the absolute value of i is the maximum and the sign of the relative difference E _r ⁱ with respect to i.
The reliability enhancement factor calculation unit calculates the reliability enhancement factor RCF using the simulation evaluation factor RF.
RCF = (3-2 · RF) ^1/2
Asking for,
The simulation result correcting apparatus according to claim 1, wherein the apparatus is a simulation result correcting apparatus. The linear combination constant calculation unit minimizes the absolute value of (S _R −S _k ) ^T · W · (S _R −S _k ), thereby reducing the objective system sensitivity coefficient vector S _R and the linear combination vector S _Find the linear coupling constant α _i that minimizes the angle formed by _k ,
The covariance error matrix W, the in the case where the covariance error matrix W _E of a systematic covariance error matrix W _R and the experiment in order schemes are different, W _R and W two matrices _E When the sizes of the (i, j) components can be compared,
The linear combination constant calculation unit includes a linear combination constant α _i satisfying S _k ^T · W _R · S _k = S _R ^T · W _R · S _R and t _i S _i ^T · W _E · S _i = S. a constant t _i satisfying _i ^T · W _R · S _i ,
The correction index value calculation unit calculates the square RD ² of the relative error RD calculated as the correction index value,

The sign of RD is determined based on the sign of this value for i (i = 1, 2,..., N) where the absolute value of COR _ij is maximized, and the sign of the relative difference E _p ⁱ for that i As a sign obtained by multiplying by
The reliability enhancement factor calculation unit calculates the reliability enhancement factor RCF using the simulation evaluation factor RF.
RCF = (3-2 · RF) ^1/2
Asking for,
The simulation result correction apparatus according to claim 1 or 2, wherein The linear combination constant computing unit is:

Find the linear coupling constant α _i that minimizes
The correction index value calculation unit calculates a correction factor BF calculated as the correction index value.

Sought by
The reliability enhancement factor calculation unit calculates the reliability enhancement factor RCF using the simulation evaluation factor RF.
RCF = (3-2 · RF) ^1/2
Asking for,
The simulation result correction apparatus according to claim 1 or 3, wherein The linear combination constant computing unit is:

Find the linear coupling constant α _i that minimizes
The correction index value calculation unit calculates a correction factor BF calculated as the correction index value.

BF = exp (LBF)
Sought by
The reliability enhancement factor calculation unit calculates the reliability enhancement factor RCF using the simulation evaluation factor RF.
RCF = (3-2 · RF) ^1/2
Asking for,
The simulation result correction apparatus according to claim 1 or 3, wherein The reliability enhancement factor calculation unit uses the simulation evaluation factor RF to calculate the reliability enhancement factor RCF using an approximate expression,
RCF = 2-RF
Asking for,
The simulation result correcting apparatus according to claim 4, wherein the simulation result correcting apparatus is the same as the simulation result correcting apparatus according to claim 4.   The simulation result correction apparatus according to claim 1, wherein the covariance error matrix W is a unit matrix. Simulation result that automatically corrects the simulation result using the result of estimating the error included in the result of simulation using the model expressing the behavior of the object on the computer using the result of the experiment simulating the object In the correction method,
A test system measurement value storage unit storing a measurement value Q _E obtained as a result of the experiment;
An experimental system measurement value storage unit storing measurement values obtained by the simulated experimental system;
A covariance error matrix computation unit and a covariance error matrix storage unit derive and store a covariance error matrix;
An experiment system physical quantity operation unit and an experiment system operation value storage unit perform simulation calculation of the simulated experiment system and store the calculation value of the simulation calculation;
A comparison value calculation unit and a comparison value storage unit calculate and store a comparison value between the simulation calculation value and the measurement value;
The computer calculates and stores the experimental system sensitivity coefficient vector in the experimental system sensitivity coefficient vector calculation unit and the target system calculation value storage unit;
A step in which a real machine system physical quantity calculation unit and a target system calculation value storage unit perform a simulation calculation of a target real machine system and store a calculation value of the simulation calculation;
A target system sensitivity coefficient vector calculation unit and an actual system sensitivity coefficient vector storage unit calculate and store the sensitivity coefficient of the target actual system;
A linear combination constant calculating unit and a linear combination constant storage unit calculating a linear combination constant;
A step of calculating a simulation evaluation factor RF in which a simulation evaluation factor calculation unit and a simulation evaluation factor storage unit combine a simulation experiment system;
A correction index value calculation unit calculating a correction index value;
A step of a reliability enhancement factor calculation unit and a reliability enhancement factor storage unit calculating a reliability enhancement factor RCF;
A calculation error correction unit calculating a product of the correction index value and the reliability enhancement factor RCF;
A simulation result correction method characterized by comprising:

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