Simulation model accuracy improvement system, simulation model accuracy improvement method, and simulation model accuracy improvement program

Abstract

To easily identify a portion or a parameter that is a cause of deviation between an experimental result and a simulation result.SOLUTION: A simulation model accuracy improvement system includes: an experimental result data input section that inputs data on a collision experiment of an entity object; a simulation section that performs simulation of the collision experiment; a mode decomposition section that performs mode decomposition of shape / behavior data generated by the simulation section and a simulation curve group; a coefficient calculation section that calculates a coefficient for fitting the simulation curve group to an experimental result curve group; a reconstruction section that reconstructs the simulation curve group and reconstructs virtual shape / behavior data; and a force output section that calculates force required for correcting a difference between the shape / behavior data and the virtual shape / behavior data.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a system for improving accuracy of a simulation model, a method for improving accuracy of a simulation model, and a program for improving accuracy of a simulation model. [Background technology]

[0002] Crash safety is very important in the automobile industry, and currently, crash safety is verified through actual vehicle testing. In actual vehicle testing, "safety" is defined as a vehicle with a specified occupant dummy at a specified speed and angle, and the acceleration and force acting on the dummy are below a standard. However, in the real world, speeds, angles, and occupant body types vary, and there is a movement to change the definition of "safety" to one that takes these factors into account and corresponds to the real world.

[0003] However, conducting multiple experiments would significantly increase costs, so in order to achieve the definition of "safety" described above, it is necessary to utilize simulations at a highly accurate and predictable level in automotive development.

[0004] However, even though simulations can achieve a high level of accuracy and predictability, there are still discrepancies with experimental results. Therefore, in order to define "safety" that corresponds to the real world as described above, it is necessary to show the degree to which the simulation results can be trusted.

[0005] ISO18571 (formerly CORA) is a scheme for comparing experimental and simulation results. In ISO18571, the simulation results are quantified and evaluated based on whether the curve is within a corridor represented by two curves, whether the shapes of the experimental curve and the simulation result curve are similar, whether there is a phase shift, whether the sizes match, etc. Additionally, the injury level of the occupant dummy is evaluated using injury values ​​calculated from multiple curves. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] TECHNICAL SPECIFICATION ISO / TS 18571,First edition 2014-08-01, Road vehicles - Objective rating metric for non-ambiguous signals

[0007] Additionally, the evaluation process at EuroNCAP, a European evaluation organization, will also evaluate the reliability of simulation results based on ISO18571 using a numerical value indicating the degree of agreement between a number of curves, and it is becoming increasingly important for automobile manufacturers to obtain a numerical value above a certain level in this evaluation. Summary of the Invention [Problem to be solved by the invention]

[0008] However, identifying the locations and parameters that cause the discrepancy between experimental and simulation results is an extremely difficult task, and the current situation is that the only way to solve the problem is through repeated trial and error.

[0009] The present invention has been made to solve the above-mentioned problems, and has an object to provide a system, a method, and a program for supporting improvement of accuracy of a simulation model, which are capable of easily identifying the locations and parameters that cause the discrepancy between experimental results and simulation results, and which are capable of improving the accuracy of a simulation model while utilizing data from past experimental results and simulation results. [Means for solving the problem]

[0010] In order to solve the above-mentioned problems, a first aspect of a simulation model accuracy improvement support system of the present invention includes: an experiment result data input unit for inputting experiment result data in a collision experiment of an actual object such as a vehicle; a simulation unit that performs a simulation of a collision experiment for the real object by using CAE analysis software based on a finite element method, for a virtual object corresponding to the real object; a mode decomposition unit that performs mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the shape / behavior of the virtual object and a group of simulation curves representing changes in a predetermined location of the virtual object including an acceleration output or a displacement output or the like of the predetermined location; a coefficient calculation unit that compares a group of experimental result curves that represent changes in a predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, included in the experimental result data, with the group of simulation curves, and calculates coefficients that allow the group of simulation curves to fit to the group of experimental result curves; a reconstruction unit that reconstructs the group of simulation curves based on the calculated coefficients and also reconstructs virtual shape and behavior data that is expected to have occurred in the crash experiment based on the calculated coefficients; and a force output unit that compares the shape / behavior data created by the simulation unit with the shape / behavior data reconstructed by the reconstruction unit and calculates or estimates the force required to correct the difference.

[0011] According to a first aspect of the simulation model accuracy improvement support system of the present invention, a coefficient for fitting the simulation curve group to the experimental result curve group is calculated, the simulation curve group is reconstructed based on the calculated coefficient, and virtual shape and behavior data expected to have occurred in the collision experiment is reconstructed based on the calculated coefficient, and the shape and behavior data is compared with the virtual shape and behavior data, and a force required to correct the difference is calculated or estimated. Therefore, it is possible to provide a simulation model accuracy improvement support system that can easily identify the location or parameter that is the cause of the discrepancy between the experimental result and the simulation result, and can improve the accuracy of the simulation model.

[0012] In another aspect of the simulation model accuracy improvement support system of the present invention, a correlation calculation unit is provided that calculates the correlation between the mode decomposed group of simulation curves and shape / behavior data and the parameters of the virtual object including plate thickness, etc. According to this other aspect, mode decomposition is performed on the shape / behavior data and the group of simulation curves, and the correlation between the mode decomposed group of simulation curves and shape / behavior data and the parameters of the virtual object is calculated. Therefore, the relationship between the main behavior modes and the input parameters that affect them can be easily understood, and it becomes possible to evaluate and examine the difference in the results of intuitive expressions such as the behavior of the head by quantifying them.

[0013] In a second aspect of the simulation model accuracy improvement support system of the present invention, an experiment result data input unit for inputting experiment result data in a collision experiment of an actual object such as a vehicle; a simulation unit that performs a simulation of a collision experiment for the real object by using CAE analysis software based on a finite element method, for a virtual object corresponding to the real object; a mode decomposition unit that performs mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the behavior of the virtual object and a group of simulation curves representing changes in the predetermined location of the virtual object including an acceleration output or a displacement output or the like of the predetermined location; a difference pattern vector extraction unit that compares a group of experiment result curves that represent changes in a predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, included in the experiment result data, with a group of simulation curves, and extracts a difference pattern vector; a coefficient calculation unit that compares the group of experimental result curves with the group of simulation curves and calculates coefficients for fitting the group of simulation curves to the group of experimental result curves; a reconstruction unit that reconstructs the group of simulation curves based on the calculated coefficients and also reconstructs virtual shape and behavior data that is expected to have occurred in the crash experiment based on the calculated coefficients; a force output unit that compares the shape and behavior data created by the simulation unit with the shape and behavior data reconstructed by the reconstruction unit, and calculates or estimates a force required to correct the difference; a knowledge database unit for storing data including past experiment results, past simulation results, past proposal reports, and information regarding differences between past experiment results and simulations; a similarity determination unit that determines a similarity between the experimental result and a simulation based on the experimental result data, the shape and behavior data created by the simulation unit, the shape and behavior data reconstructed by the reconstruction unit, or data from the knowledge database unit; and a suggestion unit that extracts countermeasures in cases where there is a high degree of similarity between the experimental results and the simulation based on the output of the similarity judgment unit, based on the data in the knowledge database unit, and proposes how to change the parameters of the virtual object, including plate thickness, etc., in order to increase a specified evaluation score.

[0014] According to a second aspect of the simulation model accuracy improvement support system of the present invention, in addition to the first aspect described above, the system judges the degree of similarity between the current experiment result and the simulation based on the experiment result data, the shape and behavior data created by the simulation unit, the shape and behavior data reconstructed by the reconstruction unit, or the data from the knowledge database unit. Then, measures in cases where the degree of similarity between the current experiment result and the simulation is high are extracted based on the data from the knowledge database unit, and a proposal is made on how to change the parameters to increase the predetermined evaluation score. Therefore, a specific proposal is made to bring the simulation result closer to the experiment result, so that a simulation model accuracy improvement support system that makes it easier for the user to solve problems and improves the accuracy of the simulation model can be provided.

[0015] In another aspect of the simulation model accuracy improvement support system of the present invention, a deviation visualization unit which, based on the shape and behavior data created by the simulation unit and the shape and behavior data reconstructed by the reconstruction unit, vectorizes information on a group of node coordinates in a mode space of a predetermined member in the shape and behavior data, and calculates a distance or direction in the mode space by decomposing the vector into modes, and visualizes the degree of deviation of the shape and behavior of the predetermined member from the direction of the difference pattern vector, whether or not the mode coordinate values ​​are close at a predetermined timing; the suggestion unit extracts measures for cases in which there is a high degree of similarity between the results of the current experiment and the simulation based on an output from at least one of the similarity determination unit and the deviation visualization unit, based on data from a knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. According to this aspect, in addition to the second aspect, information on the node coordinate group in the mode space of a predetermined member in the shape-behavior data is vectorized, and the vector is decomposed into modes to calculate the distance or direction in the mode space, and the deviation of the behavior of the predetermined member is visualized depending on whether the mode coordinate values ​​are close at a predetermined timing. Also, based on the output of at least one of the similarity determination unit and the deviation visualization unit, measures in cases where there is a high degree of similarity between the difference between the current experimental result and the simulation are extracted based on data in a knowledge database, and a proposal is made on how to change the parameters to increase the predetermined evaluation score. Therefore, a specific proposal is made to bring the simulation result closer to the experimental result, making it easier for the user to solve problems, and a simulation model accuracy improvement support system that can improve the accuracy of the simulation model can be provided.

[0016] In another aspect of the simulation model accuracy improvement support system of the present invention, The similarity determination unit is a deviation pattern similarity determination unit that determines a similarity of a deviation pattern of a curve based on the difference pattern vector; a deformation mode similarity determination unit which, based on the shape and behavior data created by the simulation unit and the shape and behavior data reconstructed by the reconstruction unit, vectorizes information on a group of node coordinates in a mode space in the shape and behavior data, and calculates a distance or direction in the mode space by decomposing the vector into modes, and determines a similarity of the deformation modes at a predetermined timing based on whether the mode coordinate values ​​are close to each other; the suggestion unit extracts measures for cases in which there is a high degree of similarity between the results of the current experiment and the simulation based on an output from at least one of the deviation pattern similarity determination unit, the deformation mode similarity determination unit, and the deviation visualization unit, based on data from a knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. According to this aspect, in addition to the other aspects, a difference pattern vector between a group of experimental result curves and a group of simulation curves is extracted, and the similarity of the deviation pattern of the curves is judged based on the difference pattern vector, and information of a group of node coordinates in a mode space in the shape and behavior data is vectorized, and the vector is decomposed into modes to calculate a distance or direction in the mode space, and the similarity of the deformation mode is judged at a predetermined timing by whether the mode coordinate values ​​are close or not. Also, based on the output of at least one of the deviation pattern similarity judgment unit, the deformation mode similarity judgment unit, and the deviation visualization unit, a countermeasure in a case where the similarity of the difference between the current experimental result and the simulation is high is extracted based on data in a knowledge database, and a proposal is made on how to change the parameters to increase the predetermined evaluation score. Therefore, a user can learn specific measures for bringing the simulation results closer to the experimental results based on the similarity of the deviation patterns of the curves, or based on the similarity of the deformation modes, or based on the output of the deviation visualization unit, or based on at least one of these, making it easier to solve problems and providing a simulation model accuracy improvement support system that can improve the accuracy of the simulation model.

[0017] In another aspect of the simulation model accuracy improvement support system of the present invention, a correlation calculation unit is provided that calculates the correlation between the mode decomposed group of simulation curves and shape / behavior data and the parameters of the virtual object including plate thickness, etc. In this other aspect, mode decomposition is performed on the shape / behavior data and the group of simulation curves, and the correlation between the mode decomposed group of simulation curves and shape / behavior data and the parameters of the virtual object is calculated. Therefore, the relationship between the main behavior modes and the input parameters that affect them can be easily understood, and it becomes possible to evaluate and examine the difference in the results of intuitive expressions such as the behavior of the head by quantifying them.

[0018] In another aspect of the simulation model accuracy improvement support system of the present invention, the system further includes an automatic execution unit that automatically executes the countermeasures proposed by the suggestion unit. In this other aspect, the user can more easily identify the parts or parameters that cause the discrepancy between the experimental results and the simulation results, and can improve the accuracy of the simulation model while using data of past experimental results and simulation results.

[0019] In order to solve the above-mentioned problems, a first aspect of a method for supporting improvement of accuracy of a simulation model according to the present invention includes: inputting, by an experiment result data input unit, experiment result data of a collision experiment of an actual object such as a vehicle; A simulation unit performs a simulation of a collision test for the collision test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; a step of mode decomposing the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the shape / behavior of the virtual object and a group of simulation curves representing changes in the predetermined locations of the virtual object, including an acceleration output or a displacement output or the like of the predetermined locations, by mode decomposition; a coefficient calculation unit comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; a step of reconstructing the group of simulation curves based on the calculated coefficients by a reconstruction unit, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; A step of comparing the shape and behavior data created by the simulation unit with the shape and behavior data reconstructed by the reconstruction unit by a force output unit, and calculating or estimating a force required to correct the difference; Equipped with.

[0020] A first aspect of the method for supporting improvement of accuracy of a simulation model of the present invention includes calculating coefficients for fitting the group of simulation curves to the group of experimental result curves, reconstructing the group of simulation curves based on the calculated coefficients, and reconstructing virtual shape and behavior data that is expected to have occurred in the collision experiment based on the calculated coefficients, and further comparing the shape and behavior data with the virtual shape and behavior data to calculate or estimate a force required to correct the difference. Therefore, it is possible to provide a method for supporting improvement of accuracy of a simulation model that can easily identify the locations and parameters that cause the discrepancy between the experimental results and the simulation results and improve the accuracy of the simulation model.

[0021] Another aspect of the method for supporting improvement of accuracy of a simulation model of the present invention includes a step of calculating, by a correlation calculation unit, a correlation between the mode-decomposed group of simulation curves and shape / behavior data and parameters of the virtual object including plate thickness, etc. According to this other aspect, mode decomposition is performed on the shape / behavior data and the group of simulation curves, and a correlation is calculated between the mode-decomposed group of simulation curves and shape / behavior data and parameters of the virtual object. Therefore, the relationship between the main behavior modes and the input parameters that affect them can be easily understood, and it becomes possible to evaluate and examine the difference in the results of intuitive expressions such as the behavior of the head by quantifying them.

[0022] In order to solve the above-mentioned problems, a second aspect of a method for supporting improvement of accuracy of a simulation model according to the present invention includes: inputting, by an experiment result data input unit, experiment result data of a collision experiment of an actual object such as a vehicle; A simulation unit performs a simulation of a collision test for the collision test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; a step of performing mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data expressing the behavior of the virtual object and the group of simulation curves expressing changes in the predetermined locations of the virtual object, including an acceleration output or a displacement output or the like of the predetermined locations; a step of comparing a group of experimental result curves, which include an acceleration output or a displacement output or the like of the predetermined portion of the substantial object obtained by the collision experiment and are included in the experimental result data, with the group of simulation curves, and extracting a difference pattern vector; a coefficient calculation unit comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; a step of reconstructing the group of simulation curves based on the calculated coefficients by a reconstruction unit, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; A step of comparing the shape and behavior data created by the simulation unit with the shape and behavior data reconstructed by the reconstruction unit by a force output unit, and calculating or estimating a force required to correct the difference; a step of determining a similarity regarding the difference between the current experiment result and the simulation by a similarity determination unit based on data from a knowledge database unit that stores the experiment result data, the shape and behavior data created by the simulation unit, the shape and behavior data reconstructed by the reconstruction unit, or past experiment results, past simulation results, past proposal reports, and data including information regarding the difference between past experiment results and simulations; A proposal unit extracts countermeasures for cases where there is a high degree of similarity between the difference between the current experimental result and the simulation based on the output of the similarity judgment unit, based on the data in the knowledge database, and proposes how to change the parameters to increase a specified evaluation score.

[0023] In addition to the above-mentioned first aspect, the second aspect of the method for supporting improvement of a simulation model of the present invention judges the degree of similarity between the difference between the current experiment result and the simulation based on the experimental result data, the shape and behavior data created by the simulation unit, the shape and behavior data reconstructed by the reconstruction unit, or the data from the knowledge database unit. Then, measures in cases where the degree of similarity between the difference between the current experiment result and the simulation is high are extracted based on the data from the knowledge database unit, and a proposal is made on how to change the parameters of the virtual object, including the plate thickness, etc., to increase a predetermined evaluation score. Therefore, a specific proposal is made to bring the simulation result closer to the experimental result, so that a method for supporting improvement of a simulation model that can improve the accuracy of the simulation model can be provided, which makes it easier for the user to solve problems.

[0024] In another aspect of the method for supporting improvement of accuracy of a simulation model of the present invention, a deviation visualization unit, based on the shape and behavior data created by the simulation unit and the shape and behavior data reconstructed by the reconstruction unit, vectorizing information of a group of node coordinates in a mode space of a predetermined member in the shape and behavior data, and calculating a distance or direction in the mode space by decomposing the vector into modes, and visualizing whether or not the mode coordinate values ​​are close at a predetermined timing and the deviation of the shape and behavior of the predetermined member from the direction of the difference pattern vector; The suggesting step extracts measures for cases where there is a high degree of similarity between the experimental results and the simulation based on the output of at least one of the similarity determining unit and the deviation visualizing unit, based on the data in the knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. According to this aspect, in addition to the second aspect, information on a group of nodal coordinates in a mode space of a predetermined component in the shape-behavior data is vectorized, and the vector is decomposed into modes to calculate a distance or direction in the mode space, and the degree of deviation of the behavior of the predetermined component is visualized depending on whether the mode coordinate values ​​are close at a predetermined timing. Also, based on the output of at least one of the similarity determination unit and the deviation visualization unit, measures in cases where there is a high degree of similarity between the experimental results and the simulation are extracted based on the data in the knowledge database unit, and a proposal is made on how to change the parameters to increase the predetermined evaluation score. Therefore, specific proposals are made to bring the simulation results closer to the experimental results, making it easier for users to solve problems and providing a simulation model accuracy improvement support system that can improve the accuracy of the simulation model.

[0025] In another aspect of the method for supporting improvement of accuracy of a simulation model of the present invention, The step of determining the similarity includes: a step of determining a similarity of deviation patterns of curves based on the difference pattern vector by a deviation pattern similarity determining unit included in the similarity determining unit; a deformation mode similarity determination unit provided in the similarity determination unit, based on the shape and behavior data created by the simulation unit and the shape and behavior data reconstructed by the reconstruction unit, vectorizing information of a group of node coordinates in a mode space in the shape and behavior data, and calculating distances in the mode space by decomposing the vectors into modes, and determining the similarity of the deformation modes based on whether or not the mode coordinate values ​​are close at a predetermined timing; The suggesting step is based on the output of at least one of the deviation pattern similarity judgment unit, the deformation mode similarity judgment unit, and the deviation visualization unit, and extracts countermeasures for cases in which there is a high degree of similarity between the experimental results and the simulation based on data in the knowledge database, and suggests how to change the parameters to increase the specified evaluation score. According to this aspect, in addition to the other aspects, a difference pattern vector between a group of experimental result curves and a group of simulation curves is extracted, and the similarity of the deviation pattern of the curves is judged based on the difference pattern vector, and information of a group of node coordinates in a mode space in the shape and behavior data is vectorized, and the vector is decomposed into modes to calculate a distance or direction in the mode space, and the similarity of the deformation mode is judged at a predetermined timing by whether the mode coordinate values ​​are close or not. Also, based on the output of at least one of the deviation pattern similarity judgment unit, the deformation mode similarity judgment unit, and the deviation visualization unit, a countermeasure in a case where the similarity of the difference between the current experimental result and the simulation is high is extracted based on data in a knowledge database, and a proposal is made on how to change the parameters to increase the predetermined evaluation score. Therefore, a user can learn specific measures for bringing the simulation results closer to the experimental results based on the similarity of the deviation patterns of the curves, or based on the similarity of the deformation modes, or based on the output of the deviation visualization unit, or based on at least one of them, making it easier to solve problems and providing a method for supporting improvement of the accuracy of a simulation model that can improve the accuracy of the simulation model.

[0026] Another aspect of the method for supporting improvement of accuracy of a simulation model of the present invention includes a step of calculating, by a correlation calculation unit, a correlation between the mode-decomposed group of simulation curves and shape / behavior data and parameters of the virtual object including plate thickness, etc. In this other aspect, mode decomposition is performed on the shape / behavior data and the group of simulation curves, and a correlation is calculated between the mode-decomposed group of simulation curves and shape / behavior data and parameters of the virtual object. Therefore, the relationship between the main behavior modes and the input parameters that affect them can be easily understood, and it becomes possible to evaluate and examine the difference in the results of intuitive expressions such as the behavior of the head by quantifying them.

[0027] In another aspect of the method for supporting improvement of accuracy of a simulation model of the present invention, the method further comprises a step of automatically executing the countermeasures proposed by the suggestion unit by an automatic execution unit. In this other aspect, the user can more easily identify the parts or parameters that cause the discrepancy between the experimental results and the simulation results, and can improve the accuracy of the simulation model while using data of past experimental results and simulation results.

[0028] In order to solve the above-mentioned problems, a first aspect of a simulation model accuracy improvement support program of the present invention includes: A program for supporting improvement of accuracy of a simulation model, the program being configured to: A step of inputting experimental result data in a collision experiment of a real object such as a vehicle; performing a simulation of a crash test for the crash test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; A step of mode decomposing shape / behavior data and a group of simulation curves, i.e., shape / behavior data representing a shape / behavior of the virtual object and a group of simulation curves representing changes in a predetermined location of the virtual object, including an acceleration output or a displacement output, etc., of the predetermined location, by mode decomposition; a step of comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; reconstructing the simulation curves based on the calculated coefficients, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; and comparing the shape and behavior data created by the simulation with the reconstructed shape and behavior data, and calculating a force required to correct the difference.

[0029] A first aspect of the simulation model accuracy improvement support program of the present invention calculates coefficients for fitting the simulation curve group to the experimental result curve group, reconstructs the simulation curve group based on the calculated coefficients, and reconstructs virtual shape and behavior data that is expected to have occurred in the collision experiment based on the calculated coefficients, and further compares the shape and behavior data with the virtual shape and behavior data to calculate or estimate a force required to correct the difference. Therefore, it is possible to provide a simulation model accuracy improvement support program that can easily identify the locations and parameters that cause the discrepancy between the experimental results and the simulation results, and improve the accuracy of the simulation model.

[0030] In another aspect of the simulation model accuracy improvement support program of the present invention, The program causes the computer to The step of calculating correlations between the group of mode-decomposed simulation curves and shape / behavior data and parameters of the virtual object including plate thickness and the like is executed. According to this other aspect, the shape / behavior data and the group of simulation curves are subjected to mode decomposition, and the correlation between the group of simulation curves and the shape / behavior data subjected to the mode decomposition and the parameters of the virtual object is calculated. Therefore, the relationship between the main behavior modes and the input parameters that affect them can be easily understood, and it becomes possible to evaluate and examine the difference in the results of intuitive expressions such as the behavior of the head by quantifying them.

[0031] In order to solve the above-mentioned problems, a second aspect of the simulation model accuracy improvement support program of the present invention includes: A program for supporting improvement of accuracy of a simulation model, the program being configured to: A step of inputting experimental result data in a collision experiment of a real object such as a vehicle; performing a simulation of a crash test for the crash test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; A step of mode decomposing shape / behavior data and a group of simulation curves, i.e., shape / behavior data expressing behavior of the virtual object and a group of simulation curves expressing changes in the predetermined locations of the virtual object, including acceleration output or displacement output, etc., of the predetermined locations, by mode decomposition; a step of comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object obtained by the collision experiment, including an acceleration output or a displacement output or the like of the predetermined location, with the group of simulation curves, and extracting a difference pattern vector; a step of comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; reconstructing the simulation curves based on the calculated coefficients, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; comparing the shape and behavior data generated by the simulation step with the reconstructed shape and behavior data, and calculating or estimating a force required to correct the difference; a step of determining a similarity of the difference between the current experiment result and the simulation based on the experiment result data, the shape and behavior data created by the step of performing the simulation unit, the shape and behavior data reconstructed by the step of reconstructing, or data from a knowledge database unit storing data including past experiment results, past simulation results, past proposal reports, and information regarding the difference between past experiment results and simulations; Based on the output of the step of determining the degree of similarity, the method executes a step of extracting countermeasures in cases where there is a high degree of similarity between the differences between the experimental results and the simulation, based on the data in the knowledge database, and proposing how to change the parameters to increase the specified evaluation score.

[0032] In addition to the above-mentioned first aspect, the second aspect of the simulation model accuracy improvement support program of the present invention judges the similarity between the difference between the current experiment result and the simulation based on the experiment result data, the shape and behavior data created by the simulation step, the shape and behavior data reconstructed by the reconstruction step, or the data from the knowledge database unit. Then, measures in cases where the similarity between the difference between the current experiment result and the simulation is high are extracted based on the data from the knowledge database unit, and a proposal is made on how to change the parameters of the virtual object, including the plate thickness, etc., to increase a predetermined evaluation score. Therefore, a specific proposal is made to bring the simulation result closer to the experiment result, so that a simulation model accuracy improvement support program that makes it easier for the user to solve problems and improve the accuracy of the simulation model can be provided.

[0033] In another aspect of the simulation model accuracy improvement support program of the present invention, The program causes the computer to a step of vectorizing information of a group of nodal coordinates in a mode space of a predetermined member in the shape and behavior data based on the shape and behavior data created in the step of performing the simulation and the shape and behavior data reconstructed in the step of reconstructing, calculating a distance or direction in the mode space by decomposing the vector into modes, and visualizing whether or not the mode coordinate values ​​are close at a predetermined timing and the degree of deviation of the shape and behavior of the predetermined member from the direction of the difference pattern vector; The proposed steps include: Based on the output of at least one of the steps of determining the degree of similarity and visualizing the degree of deviation, the system executes a step of extracting countermeasures in cases where there is a high degree of similarity between the experimental results and the simulation, based on the data in the knowledge database section, and proposing how to change the parameters to increase a specified evaluation score. According to this aspect, in addition to the second aspect, information on a group of nodal coordinates in a mode space of a predetermined component in the shape-behavior data is vectorized, and the vector is decomposed into modes to calculate a distance or direction in the mode space, and the degree of deviation of the behavior of the predetermined component is visualized depending on whether the mode coordinate values ​​are close at a predetermined timing. Also, based on the output of at least one of the similarity determination unit and the deviation visualization unit, measures in cases where there is a high degree of similarity between the experimental results and the simulation are extracted based on the data in the knowledge database unit, and a proposal is made on how to change the parameters to increase the predetermined evaluation score. Therefore, specific suggestions are made to bring the simulation results closer to the experimental results, making it easier for users to solve problems, and a simulation model accuracy improvement support program can be provided that can improve the accuracy of the simulation model.

[0034] In another aspect of the simulation model accuracy improvement support program of the present invention, The program causes the computer to The step of determining the similarity includes: determining a similarity of deviation patterns of the curves based on the difference pattern vector; a step of vectorizing information of a group of node coordinates in a modal space in the shape and behavior data based on the shape and behavior data created in the step of performing the simulation and the shape and behavior data reconstructed in the step of reconstructing, and calculating a distance or direction in the modal space by decomposing the vector into modes, and judging a similarity of the deformation modes based on whether the modal coordinate values ​​are close at a predetermined timing, The proposed steps include: extracting measures based on data in the knowledge database in cases where there is a high degree of similarity between the experimental results and the simulation based on output from at least one of the steps of determining the similarity of the deviation patterns, determining the similarity of the deformation modes, and visualizing the deviation, and proposing how to change the parameters to increase a predetermined evaluation score; According to this aspect, in addition to the other aspects described above, a difference pattern vector between a group of experimental result curves and a group of simulation curves is extracted, and the similarity of the deviation patterns of the curves is determined based on the difference pattern vector. At the same time, information on the group of nodal coordinates in the modal space in the shape and behavior data is vectorized, and the vector is decomposed into modes to calculate the distance or direction in the modal space, and the similarity of the deformation modes is determined by determining whether the modal coordinate values ​​are close at a predetermined timing. In addition, based on the output of at least one of the deviation pattern similarity judgment unit, the deformation mode similarity judgment unit, and the deviation visualization unit, countermeasures for cases where there is a high degree of similarity between the differences between the experimental results and the simulation are extracted based on data in a knowledge database, and a proposal is made as to how to change the parameters to increase a specified evaluation score. Therefore, a user can know specific measures for bringing the simulation results closer to the experimental results based on the similarity of the deviation patterns of the curves, or based on the similarity of the deformation modes, or based on either of them, making it easier to solve problems and providing a simulation model accuracy improvement support program that can improve the accuracy of the simulation model.

[0035] In another aspect of the simulation model accuracy improvement support program of the present invention, The program causes the computer to The step of calculating correlations between the group of mode-decomposed simulation curves and shape / behavior data and parameters of the virtual object including plate thickness and the like is executed. In another aspect, the shape / behavior data and the group of simulation curves are subjected to mode decomposition, and the correlation between the group of simulation curves and the shape / behavior data subjected to the mode decomposition and the parameters of the virtual object is calculated. Therefore, the relationship between the main behavior modes and the input parameters that affect them can be easily understood, and it becomes possible to evaluate and examine the difference in the results of intuitive expressions such as the behavior of the head by quantifying them.

[0036] In another aspect of the simulation model accuracy improvement support program of the present invention, The program causes the computer to The method further comprises the step of automatically executing the countermeasure proposed in the proposing step. In this other aspect, the user can more easily identify the locations and parameters that are causing the discrepancy between the experimental results and the simulation results, and can improve the accuracy of the simulation model while utilizing data from past experimental and simulation results. Effect of the Invention

[0037] According to the present invention, it is possible to provide a simulation model accuracy improvement support system, a simulation model accuracy improvement support method, and a simulation model accuracy improvement support program that can easily identify the locations and parameters that are causing the discrepancy between experimental results and simulation results, and that can improve the accuracy of a simulation model while utilizing data from past experimental results and simulation results. [Brief description of the drawings]

[0038] [Figure 1] 1 is a diagram showing a schematic configuration of a simulation model accuracy improvement support system according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing functional blocks of the simulation model accuracy improvement support system according to the first embodiment. [Diagram 3] FIG. 2 is a diagram showing functional blocks of a database in the simulation model accuracy improvement support system according to the first embodiment. [Figure 4] 1 is a block diagram showing a process flow in a simulation model accuracy improvement support system according to a first embodiment. FIG. [Diagram 5] (A) and (B) show the results of principal component analysis using DIFFCRASH on the node group of the head part at 100 ms. [Figure 6] The behavior of each of the two large clusters in the first modal coordinates is shown. [Figure 7] (A) and (B) show the correlation matrices between the input parameters and the modal contributions. [Figure 8] The results of the regression analysis of the input parameters and the modal coordinates are shown. [Figure 9] FIG. 2 is a diagram for explaining a mode decomposition process in the simulation model accuracy improvement support system of the first embodiment. [Figure 10] FIG. 2 is a diagram for explaining the processing of a reconstruction unit in the simulation model accuracy improvement support system according to the first embodiment. [Figure 11]4 is a diagram for explaining the processing of a deviation visualization unit in the simulation model accuracy improvement support system of the first embodiment. FIG. [Figure 12] FIG. 2 is a block diagram showing the flow of a process for extracting countermeasures in a case where there is a high degree of similarity regarding the difference between the current experiment result and the simulation result in the simulation model accuracy improvement support system of the first embodiment. [Figure 13] 4 is a diagram for explaining the processing of a deviation pattern similarity determination unit in the simulation model accuracy improvement support system of the first embodiment. FIG. [Figure 14] 4A to 4C are diagrams for explaining the processing of a deformation mode similarity determination unit in the simulation model accuracy improvement support system of the first embodiment. [Figure 15] 4A to 4C are diagrams for explaining the processing of a deformation mode similarity determination unit in the simulation model accuracy improvement support system of the first embodiment. [Figure 16] FIG. 11 is a block diagram showing the flow of processing for extracting measures in a case where there is a high degree of similarity regarding the difference between the current experiment result and the simulation result in the simulation model accuracy improvement support system according to the second embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] (First embodiment) Hereinafter, a simulation model accuracy improvement support system according to a first embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram showing a schematic configuration of a simulation model accuracy improvement support system 100 according to this embodiment. As shown in FIG. 1, the simulation model accuracy improvement support system 100 of this embodiment includes a simulation model accuracy improvement support device 110 and a server 120 connected to the simulation model accuracy improvement support device 110 via a network 200 such as the Internet. As shown in FIG. 1, the simulation model accuracy improvement support device 110 includes a central processing unit 1, a display device 2, a storage device 3, an input device 4, an output device 5, and a communication device 6.

[0040] The central processing unit 1 is a device capable of executing a program such as a personal computer, and includes a CPU and a memory. The display device 2 is a device capable of displaying characters and images such as a liquid crystal display. The storage device 3 is a device capable of storing programs and data such as a hard disk drive (HDD), and an external server or the like may be used. The program of the present invention is stored in the storage device 3. The input device 4 is a device such as a keyboard that allows a user to input data or instructions. The output device 5 is a device such as a printer that can output characters and images. In the simulation model accuracy improvement support system 100 of this embodiment, the output device 5 may be omitted. The communication device 6 is an interface that enables communication with the server 120 and other devices via the network 200.

[0041] The server 120 is used as a storage destination for metadata, which will be described later, and reports as simulation results. However, in the present invention, the storage destination for the metadata and reports does not necessarily have to be a server, and may be a local storage such as a hard disk drive (HDD), a cloud server, or a network such as a block chain.

[0042] Fig. 2 is a diagram showing functional blocks that function as a result of the central processing unit 1 of the simulation model accuracy improvement support device 110 executing the program of the present invention. As shown in Fig. 2, the central processing unit 1 functions as a control unit 10. In addition, the control unit 10 functions as an experiment result data input unit 11, a simulation unit 12, a mode decomposition unit 13, a correlation calculation unit 14, a coefficient calculation unit 15, a difference pattern vector extraction unit 16, a reconstruction unit 17, a force output unit 19, a server registration unit 20, a deviation pattern similarity determination unit 21, a deformation mode similarity determination unit 22, a deviation visualization unit 23, and a proposal unit 24 according to the program of the present invention.

[0043] In this embodiment, the program of the present invention is executable in a simulation model accuracy improvement support device 110 .

[0044] The experiment result data input unit 11 inputs, in the simulation model accuracy improvement support device 110, experiment result data of a collision experiment using a vehicle as an example of an actual object.

[0045] The simulation unit 12 performs a simulation of a collision experiment for the virtual object corresponding to the real object by using CAE analysis software based on the finite element method. In this embodiment, Ansys LS-DYNA (registered trademark) is used as an example of the CAE analysis software. In the simulation, shape and behavior data representing the shape and behavior of the virtual object and a group of simulation curves representing changes in a predetermined location of the virtual object, including an acceleration output or a displacement output, etc., of the predetermined location are created. The shape and behavior data can be visualized, for example, as a 3D animation, for user confirmation. The visualization of the shape and behavior data can be performed at any time by the user's selection.

[0046] The modal decomposition unit 13 performs modal decomposition on the shape / behavior data and the group of simulation curves created by the simulation unit 12 .

[0047] Modal decomposition is performed on the geometry and behavior data and the set of simulation curves based on the following data: Shape and behavior data and a set of simulation curves created in a simulation under the same conditions as a crash test (a crash test conducted under certain speed and angle conditions is called a "base test." Details of the "base test" will be described later.) (a simulation conducted under the same speed and angle conditions as the base test is called a "base calculation." Details of the "base calculation" will be described later.) Archive data of past shape and behavior data and archive data of past simulation curves stored in the simulation database section described later In addition to the shape / behavior data and the group of simulation curves created in one of the base calculations, it is also possible to perform another base experiment having different speed conditions and angle conditions from one of the base experiments, and to perform another base calculation corresponding to this other base experiment, and to use the shape / behavior data and the group of simulation curves created in this other base calculation as the basis. Furthermore, one or more simulations of variation cases may be performed under speed conditions and angle conditions different from those of the base experiment, and the simulation may be based on the one or more simulations of the variation cases. The mode decomposition will be described in detail later.

[0048] The correlation calculation unit 14 calculates the correlation between the mode decomposed simulation curve group and shape / behavior data and the parameters of the virtual object including plate thickness, etc. The calculation of this correlation will be described later in detail.

[0049] The coefficient calculation unit 15 compares the group of simulation curves with a group of experimental result curves that are included in the experimental result data and that represent changes at a predetermined location of the vehicle, including an acceleration output or a displacement output at the predetermined location obtained by a crash experiment, and then calculates coefficients that allow the group of simulation curves to fit the group of experimental result curves.

[0050] The difference pattern vector extraction unit 16 compares a group of experimental result curves, which are included in the experimental result data and which represent changes in the specified locations of the vehicle, including acceleration output or displacement output, etc., obtained by a collision experiment, with a group of simulation curves, and extracts a difference pattern vector.

[0051] The reconstruction unit 17 reconstructs a group of simulation curves based on the coefficients calculated as described above, and also reconstructs virtual shape and behavior data that is expected to have occurred in the crash test based on the coefficients calculated as described above.

[0052] The force output unit 19 compares the shape / behavior data with the virtual shape / behavior data, and calculates the force required to correct the difference.

[0053] The server registration unit 20 stores data such as the data obtained by the simulation model accuracy improvement support device 110, the input experimental results, and measures to correct discrepancies between the experimental results and the simulation results in a simulation database 121 and a knowledge database 122, which will be described later.

[0054] The deviation pattern similarity determining unit 21 determines the similarity of the deviation patterns of the curves based on the difference pattern vector.

[0055] The deformation mode similarity judgment unit 22 calculates the distance in the mode space between the simulation results by vectorizing the information of the nodal coordinate group in the shape and behavior data in the mode space in the shape and behavior data based on the shape and behavior data created by the simulation unit 12 and the shape and behavior data reconstructed by the reconstruction unit 17, and decomposing the vector into modes. Then, the deformation mode similarity judgment unit 22 judges the similarity of the deformation modes depending on whether the mode coordinate values ​​are close at a predetermined timing.

[0056] Based on the shape and behavior data created by the simulation unit 12 and the shape and behavior data reconstructed by the reconstruction unit 17, the deviation visualization unit 23 vectorizes information on the node coordinate group in the mode space of a predetermined member in the shape and behavior data, and calculates the distance in the mode space by decomposing the vector into modes, and quantifies the deviation. In addition, the deviation visualization unit 23 may use information on direction as well as distance to quantify the deviation. This is because the result will be different if the direction in the mode space is different even for the same distance. Then, the deviation visualization unit 23 visualizes the deviation of the behavior of the predetermined member with a contour depending on whether the mode coordinate value is close at a predetermined timing. Note that the visualization of the deviation is not limited to the method using the contour. The deviation may be expressed as a scalar value or a vector value. For example, a method of showing scalar values ​​with a color bar (a low scalar value is shown with a blue color bar, and a high scalar value is shown with a red color bar, etc.) may be adopted. Furthermore, it is also conceivable to present vector values ​​with arrows.

[0057] The suggestion unit 24 extracts measures based on the data in the knowledge database 122 in cases where the similarity between the difference between the current experimental result and the simulation is high, based on at least one output of the deviation pattern similarity judgment unit 21, the deformation mode similarity judgment unit 22, and the deviation visualization unit 23. Then, the suggestion unit 24 proposes how to change the parameters to increase the predetermined evaluation score. The suggestion unit 24 may also classify the similarity of the deviation pattern judged by the deviation pattern similarity judgment unit 21. For example, the deviation pattern is a sequence created by comparing the curves of the experiment and the simulation at a predetermined location, and it is possible to classify the patterns as similar for one curve, or as similar trends when considering deviation patterns at multiple locations as a group. In this way, similar patterns or similar trends are considered to be deviations due to similar factors, so it is possible to make similar suggestions for each of such groups. Furthermore, when making the proposal, the proposing unit 24 does not have to use all of the outputs of the deviation pattern similarity determining unit 21, the deformation mode similarity determining unit 22, and the deviation visualization unit 23, and may make the proposal using at least one of the outputs of the deviation pattern similarity determining unit 21, the deformation mode similarity determining unit 22, and the deviation visualization unit 23. In addition, when making the proposal, it is considered that there are restrictions on the materials and shape layouts that can be used. In that case, the proposing unit 24 can be configured to make the proposal while taking such restrictions into consideration. Furthermore, the proposing unit 24 may make the above-mentioned proposal taking into consideration the correlation between the simulation curve group and the shape / behavior data calculated by the correlation calculation unit 14 and the parameters of the virtual object, including the plate thickness, etc. In this way, the persuasiveness and accuracy of the proposal can be improved.

[0058] 3, the server 120 includes a simulation database 121 and a knowledge database 122. The simulation database 121 is a database that stores past simulation results by the simulation unit 12.

[0059] The knowledge database 122 is a database that stores data including past experiment results, past simulation results, past proposal reports, and information regarding differences between past experiment results and simulations. The past data stored in the knowledge database 122 does not necessarily have to be created by the user himself, and also includes data calculated by a third party in the past.

[0060] Next, an outline of the operation in this embodiment will be described with reference to Fig. 4 to Fig. 12. As shown in Fig. 4, in this embodiment, the program of the present invention includes a function for comparing the experimental results with the simulation results, and a function for proposing measures to correct the deviation of the simulation results from the experimental results.

[0061] <Comparison function between experimental and simulation results> First, the function of comparing the experimental results with the simulation results in this embodiment will be described with reference to FIGS. In this embodiment, a collision experiment is performed in which vehicles are collided at a predetermined speed and a predetermined angle, and an occupant dummy is placed in the vehicle to measure the acceleration and force acting on the dummy. As an example, a collision experiment performed under certain speed and angle conditions is called a base experiment. In this embodiment, the experiment result data input unit 11 inputs the experiment result data of the base experiment (FIG. 4: S1).

[0062] Next, the simulation unit 12 creates a virtual object corresponding to the vehicle used in the base experiment by finite element modeling (FEM) using Ansys LS-DYNA (registered trademark) as an example of CAE analysis software. The simulation unit 12 also performs a finite element simulation corresponding to the above-mentioned collision experiment. As an example, a simulation performed under the same speed conditions and angle conditions as the base experiment is called a base calculation. The simulation unit 12 performs the base calculation (FIG. 4: S2).

[0063] As a result of the above simulation, shape and behavior data representing the behavior of the virtual object and a group of simulation curves representing changes in predetermined locations of the virtual object, including acceleration output or displacement output, etc., of the said predetermined locations, are generated as simulation results.

[0064] The mode decomposition unit 13 performs mode decomposition on the shape / behavior data and the group of simulation curves created by the simulation unit 12 as described above (FIG. 4: S3). The mode decomposition is performed based on the shape / behavior data and the group of simulation curves by the base calculation, and also on the past shape / behavior data and the group of past simulation curves in the past simulations stored in the simulation database 121. Alternatively, the mode decomposition may be performed based on the shape / behavior data and the group of simulation curves created in the other base calculation by performing another base experiment with different speed conditions and angle conditions from a certain base experiment, as described above. In addition, the mode decomposition may be performed based on the simulation of one or more variation cases under speed conditions and angle conditions different from those of the base experiment, and the simulation of the one or more variation cases.

[0065] Next, the difference pattern vector extraction unit 16 compares a group of experimental result curves that represent changes in specific locations of the vehicle, including acceleration output or displacement output at the specific locations obtained by the collision experiment and that are included in the experimental result data of the base experiment, with a group of simulation curves obtained by simulating the base calculation, and extracts a difference pattern vector (FIG. 4: S4). The reason for extracting the difference pattern vector is to lower the resolution to make it easier to find similar patterns, since the curve has a very complex shape. For example, a phenomenon over a period of 150 ms can be divided into 30 ms intervals, the difference in the curves can be integrated, and then the average can be calculated by dividing the difference by the unit time of 30 ms. As shown in Figure 13 below, the curve has a very complex shape, so the intention is to lower the resolution and show it as a sequence. This is an example of a "difference pattern vector."

[0066] Next, the correlation calculation unit 14 calculates the correlation between the group of simulation curves and the shape / behavior data mode-decomposed by the mode decomposition unit 13 in step S4 and the parameters of the virtual object including the plate thickness and the like (FIG. 4: S5).

[0067] In this embodiment, the correlation is calculated by using, as an example, DIFFCRASH, which is software developed by SIDACT, a German company. DIFFCRASH is software that visualizes the degree of variation based on statistical methods from multiple calculation results using a finite element method solver such as Ansys LS-DYNA (registered trademark) (DIFFCRASH reference (1): R. Brown, M. Bloomfield, C. Thole, L. Nikitina, “Analysis of the Scatter of a Deploying Airbag”, 12th International LS-DYNA Users Conference, 2012, DIFFCRASH reference (2): D. Borsotto et al.: “Improving robustness of Chevrolet Silverado with exemplary design adaptations based on identified scatter sources”, 10th European LS-DYNA Conference, 2015).

[0068] By using DIFFCRASH, the following can be achieved: -Detect key events and points of interest - Reduce the dimension of the behavior when an event occurs by using mode decomposition - Visualize the relationships between input variables, intermediate variables (behavior mode), and output variables by connecting them in chronological order

[0069] In the following, as a specific example, an example of an automobile driver being out of position will be described. In this example, the friction between the airbag and the dummy and the position of the dummy are used as parameters.

[0070] (STEP 1) First, based on the shape and behavior data, the main events and the areas to be focused on are extracted. For example, the behavior of the dummy head is taken as the main event and the area to be focused on.

[0071] (STEP 2) Next, a modal analysis of the dummy head behavior at 100 ms (final state), which is the main event, is performed using principal component analysis. This process reduces the dimensions of each calculation result to a two- or three-dimensional position vector. Figures 5 (A) and (B) show the results of principal component analysis using DIFFCRASH on the node group of the head part at 100 ms.

[0072] From the eigenvalue ranking in Figure 5(A), it can be seen that in this example, the first mode is dominant compared to the second and subsequent modes. Additionally, each point in Figure 5(B) represents one simulation result, and the coordinates of the first and second mode axes are the contribution rates that indicate whether the behavior in each result can be expressed as a superposition of modes. From Figure 5(B), it can be seen that in this example, two large clusters are observed on the left and right of the first mode coordinate, indicating that bifurcation has occurred in the behavior.

[0073] The behavior of each cluster is shown in Figure 6. The cluster on the negative side of the primary mode axis is a mode in which the dummy is pushed away by the airbag (push-away mode), and the cluster on the positive side is a mode in which the dummy's head is pressed down by the airbag (push-down mode).

[0074] (STEP 3) Next, we performed a regression analysis on the input parameters and modes. Fluctuations in parameters affect the behavior of the main parts for each event. This is shown as the mode contribution rate in the graph, and by performing a regression analysis, we can evaluate which parameters have the greatest impact on the behavior. First, we analyzed the relationship between the input parameters and the mode contribution rate.

[0075] Figures 7(A) and (B) show the correlation matrices between the input parameters and the modal contribution ratios. Figures 7(A) and (B) show that the position (DX_DMY) and friction (MU_PD) have a strong correlation only with the first mode.

[0076] In this example, the influence of the input variability of the dummy position and the friction coefficient between the dummy and the airbag on the coordinates in the mode space is obtained by sensitivity analysis using regression analysis. As a result of the regression analysis, when the dummy position is low or the friction coefficient is low, the mode on the left side of Figure 8 occurs stably, but when it moves even slightly upward from the zero point of the base calculation or the friction coefficient increases from the initial friction of 0.3, the mode on the right side also begins to occur, and the results begin to diverge. Bifurcating modes indicate an increase in the uncertainty of the results that will occur during use. This means that the design cost to deal with the bifurcation will increase, so it is necessary to review the design and stabilize the behavior.

[0077] In this way, it is possible to visualize the relationship between the main behavior modes and the input parameters that affect them. With this method, it is possible to quantify the difference in results for intuitive expressions such as head behavior and evaluate and examine them.

[0078] Next, the coefficient calculation unit 15 compares a group of experimental result curves that are included in the base experiment and that express changes in the predetermined locations of the vehicle, including acceleration output or displacement output, etc., obtained by a crash experiment, with a group of simulation curves of the base calculation. Then, the coefficient calculation unit 15 calculates coefficients that fit the group of simulation curves by the simulation of the base calculation to the group of experimental result curves of the base experiment (FIG. 4: S6).

[0079] As shown in Fig. 9(A), a difference occurs between the simulation curve and the curve of the experimental results. Therefore, the mode decomposition unit 13 decomposes the simulation curve into modes as shown in Fig. 9(B). Then, the coefficient calculation unit 15 multiplies the decomposed mode by a coefficient as shown in Fig. 9(C) and adds the adjusted mode, thereby fitting the simulation curve to the experimental results curve.

[0080] Next, the reconstruction unit 17 reconstructs a group of simulation curves based on the coefficients calculated as described above, and also reconstructs virtual shape and behavior data that is expected to have occurred in the base experiment based on the coefficients calculated as described above (FIG. 4: S6).

[0081] Reconstruction is performed by multiplying the decomposed mode vectors by coefficients obtained by optimization calculations and then adding them up. One possible example is the reconstruction technology (Reference: European Patent No. 2924585) from SIDACT, a German company, shown in Fig. 10. This makes it possible to reconstruct vehicle behavior that cannot be fully measured in experiments as shape and behavior data.

[0082] Next, the force output unit 19 compares the shape and behavior data of the base calculation with the virtual shape and behavior data corresponding to the base experiment reconstructed as described above, and calculates the force required to correct the difference (FIG. 4: S7).The force output unit 19 also compares the shape and behavior data obtained from the calculation with the virtual shape and behavior data corresponding to the experiment reconstructed as described above, and calculates the force required to correct the difference (FIG. 4: S7).

[0083] This force output is processed by calculating the force required to fill the difference in behavior between the virtual shape and behavior data corresponding to the reconstructed base experiment and the shape and behavior data of the base calculation, and visualizing it as a contour (Figure 11). One example is the Gray-Box-Processing technology of the Fraunhofer EMI Laboratory in Germany (Reference 1: Grey-Box-Processing': a novel validation method for use in vehicle safety applications, Thomas Soot1, Michael Dlugosch, Jens Fritsch, Noriyo Ichinose, Stefan Hiermaier, Fabian Duddeck, Published: 18 April 2022, volume 39, pages2677-2698 (2023), Reference 2: JP2023-524694). The Gray-Box-Processing technology uses data assimilation technology to calculate the force required to fill the difference in behavior between the experiment and the simulation, and visualizes it as a contour. In this embodiment, this technology is applied to calculate the force required to fill the difference between the virtual shape and behavior data corresponding to the base experiment reconstructed as described above and the behavior of the shape and behavior data of the base calculation, and visualize it as a contour. By performing such processing, it becomes possible to show the user hints on measures required to bring the parts where the simulation results deviate from the experimental results closer to the experimental results.

[0084] <Function to propose measures to correct discrepancies between simulation results and experimental results> Next, the function of proposing measures to correct the deviation of the simulation results from the experimental results in this embodiment will be described with reference to Fig. 4 and Fig. 12 to Fig. 15. In this embodiment, as shown in Fig. 4, by referring to past data stored in the knowledge database 122 (Fig. 4: S9), cases with high similarity regarding the difference between the current experimental result and the simulation are extracted, and further measures for those cases are extracted (Fig. 4: S10).

[0085] Details of step S10 in Fig. 4 are shown in Fig. 12. First, the deviation pattern similarity determination unit 21 determines the similarity of the deviation patterns of the curves based on the difference pattern vector extracted in step S5 in Fig. 4 (Fig. 12: S21).

[0086] In this way, the similarity of deviation patterns of curves is judged by quantifying the deviation patterns of the experiment and simulation as shown in Fig. 13, and comparing whether the deviation pattern in the current calculation result (Fig. 12: S20) is similar to the deviation pattern stored in the experiment result database 123 of the knowledge database 122. Specifically, the deviation pattern is extracted as a difference pattern vector, and the similarity is evaluated by a comparison method such as the inner product with the difference pattern vector extracted from the same part. This is based on the idea that if the deviations are similar in the same output channel, the factors of the deviations are likely to be similar as well.

[0087] Next, the deformation mode similarity judgment unit 22 vectorizes information on the nodal coordinate group in the mode space in the shape and behavior data by the simulation based on the current calculation result (S20 in FIG. 12) and data from the experiment result database 123 in the knowledge database 122. Then, as shown in FIG. 14, the deformation mode similarity judgment unit 22 calculates the distance in the mode space by decomposing the vector into modes. In this way, the deformation mode similarity judgment unit 22 judges the similarity of the deformation modes depending on whether the mode coordinate values ​​are similar at a predetermined timing (S21 in FIG. 12). This is based on the idea that if the mode coordinate values ​​are similar at the same timing, the behaviors are also similar.

[0088] Next, based on the current calculation result (S20 in FIG. 12) and data from the experiment result database 123 in the knowledge database 122, the deviation visualization unit 23 vectorizes information on the node coordinate group in the mode space of a specific member in the shape and behavior data by the simulation. Then, as shown in FIG. 15, the deviation visualization unit 23 calculates the distance or direction in the mode space by decomposing the vector into modes. In other words, the idea of ​​vectorizing information on the node coordinate group in the shape calculated by the finite element method and decomposing the vector into modes can be applied not only to the entire shape: the entire product (e.g., when considering the entire automobile body as one), but also partially (e.g., narrowing down to only a certain member or a certain assembly (e.g., bumper)). Even if the movement appears to be the same when considering the entire car body, only a certain part may move differently. In such cases, local differences may be overlooked by only looking at the whole. However, such oversights can be prevented by evaluating each part. In this way, the deviation visualization unit 23 visualizes the deviation of the behavior of the specified member as a contour depending on whether the modal coordinate values ​​are close at a specified timing (FIG. 12: S21). By increasing the resolution in this way, it is possible to perform a similarity search, such as searching for similar behaviors of certain parts. As described above, the visualization of the deviation is not limited to the method using contours. The deviation may be expressed as a scalar value or a vector value. For example, a method of showing the scalar value with a color bar (a blue color bar when the scalar value is low, and a red color bar when the scalar value is high) may be adopted. Furthermore, it is also possible to present the vector value with an arrow.

[0089] When the search for the curve deviation pattern, the similarity of the deformation mode, and the portion with large deviation (S21 in FIG. 12) is completed in this manner, a comparison is made with past problem cases in the trouble response database 124 of the knowledge database 122 based on the above-mentioned curve deviation pattern, the similarity of the deformation mode, and the portion with large deviation, and an evaluation is performed (S22 in FIG. 12). Specifically, information close to the result obtained in step S21 is searched for among information on past curve deviation patterns, the similarity of the deformation mode, and the portion with large deviation in the trouble response database 124.

[0090] Then, the suggestion unit 24 extracts and lists countermeasures for cases with high similarity regarding the difference between the current experimental result and the simulation based on the data of the trouble response database 124 of the knowledge database 122 based on the case output with high similarity regarding the difference between the current experimental result and the simulation based on at least one output of the deviation pattern similarity judgment unit 21, the deformation mode similarity judgment unit 22, and the deviation visualization unit 23 (FIG. 12: S23). For example, the trouble response database may store reports of past countermeasures and the difference pattern vectors at that time in association with each other, and may combine a pattern recognition AI that has been trained to determine that the associated pattern and the current pattern are similar, with a summary AI and a suggestion AI that summarize the associated report when it is determined to be similar and present it to the user. The suggestion unit 24 also outputs documents of countermeasure success cases and documents of countermeasure failure cases in order of effectiveness based on past reports and comments stored in the trouble response database 124 (FIG. 12: S24).

[0091] Then, based on the parameter correlation calculated in step S6 of FIG. 4, as well as documents of countermeasure success cases and documents of countermeasure failure cases, the proposal unit 24 proposes a candidate list of how to change the parameters to increase a specified evaluation score (ISO18571) (FIGS. 4 and 12: S8).

[0092] In this way, according to this embodiment, a candidate list of parameters that can be changed to increase a given evaluation score (ISO18571) is presented to the user. Therefore, the user can easily identify the location or parameter that is causing the discrepancy between the experimental results and the simulation results, and can improve the accuracy of the simulation model while using data from past experimental and simulation results.

[0093] Then, the user makes changes based on the above suggestions, and then feeds back results and reports on whether the deviation between the experimental results and the simulation results was reduced or not to the simulation database 121 and the knowledge database 122 (FIG. 4: S11, S12). By doing this, more data is accumulated, and the accuracy of the simulation model can be improved.

[0094] Second embodiment Next, a second embodiment of the present invention will be described with reference to FIG.

[0095] In the first embodiment, the suggestion unit 24 proposes a candidate list of parameter changes that will increase the ISO18571 score, and the user judges and implements the suggestions.

[0096] However, in this embodiment, an automatic execution unit is provided, and as shown in Fig. 16, the execution of the proposed measures is automatically performed by the automatic execution unit (Fig. 16: S14). Then, the automatic execution unit presents the obtained results to the user (Fig. 16: S15). In this case, the user judges the results.

[0097] Thus, according to this embodiment, not only is a candidate list of parameters that can be changed to increase the specified evaluation score (CORA) proposed, but the automatic execution unit also executes the countermeasures. Therefore, the user can more easily identify the parts and parameters that cause the discrepancy between the experimental results and the simulation results, and can improve the accuracy of the simulation model while using data from past experimental and simulation results.

[0098] (Modification) The above embodiment is an example, and various modifications are possible without departing from the scope of the present invention. In the above embodiment, an example using one base experiment and one base calculation has been described, but the present invention is not limited to such an example. For example, a second base experiment with different speed conditions and angle conditions from the first base experiment, and a second base calculation corresponding to this second base experiment may be added (FIG. 4: S1, S2). Such information may be obtained from a simulation unit or the like, or may be obtained from past data in the simulation database unit as necessary. Furthermore, simulation and mode decomposition may be performed by adding multiple variation cases with different speed conditions and angle conditions (FIG. 4: S3).

[0099] In the above embodiment, the correlation calculation unit 14 has been described as an essential component, but the present invention is not limited to this aspect. The above-mentioned simulation model accuracy improvement support system 100 may be configured without the correlation calculation unit 14. However, the inclusion of the correlation calculation unit 14 can improve the persuasiveness and accuracy of the above-mentioned proposal.

[0100] In the above embodiment, the case where SIDACT's technology is applied to the mode decomposition unit 13, correlation calculation unit 14, coefficient calculation unit 15, and reconstruction unit 17 has been described, but the present invention is not limited to such an embodiment. Technology other than SIDACT's technology may be used as long as the functions of the above-mentioned units are realized.

[0101] In the above embodiment, the force output unit 19 applies the technology of Fraunhofer to compare the shape and behavior data of the base calculation with the virtual shape and behavior data corresponding to the reconstructed base experiment, and calculates the force required to correct the difference. However, the present invention is not limited to such an embodiment. For example, after sufficient data is accumulated, the required force can be estimated by searching the knowledge database 122 using the deviation pattern in the mode space as a key. This is because if the deviation pattern is different, the pattern of insufficient or excessive force is considered to be similar, and it is possible to map the information to the target shape. In addition, as another example of force output, a method of simply calculating the required force using Newtonian mechanics of F=m*a from the difference in the position and weight of the center of gravity of the parts in each calculation can be given.

[0102] In the above-described embodiment, the case where the deviation pattern similarity determination unit 21 and the deformation mode similarity determination unit 22 are provided as an example of the similarity determination unit has been described. However, the present invention is not limited to such an embodiment. The similarity determination unit may be configured to include either the deviation pattern similarity determination unit 21 or the deformation mode similarity determination unit 22. In addition, the threshold value and weighting when determining the similarity of past cases using deviation patterns of shapes and curves may be changed by a system administrator or a user as necessary. Furthermore, the determination of the similarity may be based on strict logic or may be heuristic, such as deep learning.

[0103] In the above-mentioned embodiment, the case where both the deviation visualization unit 23 and the proposal unit 24 are provided has been described. However, the present invention is not limited to such an aspect. When at least the proposal unit 24 is provided as described above, the user can improve the accuracy of the simulation model based on the proposal by the proposal unit 24. However, when the deviation visualization unit 23 is provided, after seeing the proposal by the proposal unit 24, it is also possible to check the information of the deviation visualization unit 23 to confirm the truth. Alternatively, depending on the user, there may be cases where the problem can be solved only by the information visualized by the deviation visualization unit 23. In addition, in the middle of each of the above-mentioned processes, the results obtained in each process may be visually confirmed by the user himself / herself in response to the user's request. Furthermore, in the middle of each of the above-mentioned processes, the process may be interrupted, and recalculation, changes to the data used, etc. may be performed at the user's discretion.

[0104] In the above-described embodiment, a finite element simulation is used to evaluate the crashworthiness of a vehicle, but the present invention is not limited to such an embodiment. For example, the present invention can be applied to any evaluation that is performed by simulation, such as material property evaluation, structural evaluation, seismic resistance evaluation, and electromagnetic property evaluation.

[0105] In addition, in the above-described embodiment, a case has been described in which vehicles are used as real objects and virtual objects, but the present invention is not limited to this, and it is also possible to use airplanes, ships, or trains as real objects and virtual objects.

[0106] The simulation model accuracy improvement support program according to the above aspect can be provided in a form stored in a computer-readable recording medium and installed in a computer. The recording medium is, for example, a non-transitory recording medium, and is exemplified by an optical recording medium such as a CD-ROM, but may include any known type of recording medium such as a semiconductor recording medium or a magnetic recording medium. It is also possible to provide the above-mentioned program in the form of distribution via a communication network and install it in a computer.

[0107] The above describes the simulation model accuracy improvement support system, the simulation model accuracy improvement support method, and the simulation model accuracy improvement support system program according to an embodiment of the present invention. However, the present invention is not limited to this, and various modifications are possible without departing from the gist of the present invention. [Explanation of symbols]

[0108] 10 Control section 11 Experimental result data input section 12 Simulation Section 13 Modal decomposition section 14 Correlation calculation section 15 Coefficient calculation section 16 Difference pattern vector extraction unit 17 Reconstruction Department 19 Force output section 20 Server Registration Department 21 Deviation pattern similarity judgement unit 22 Deformation mode similarity judgment unit 23 Discrepancy visualization part 24 Proposal Department 100 Simulation model accuracy improvement support system 110 Simulation model accuracy improvement support device 121 Simulation Database 122 Knowledge Database 123 Experimental Results Database 124 Troubleshooting Database

Claims

1. an experiment result data input unit for inputting experiment result data in a collision experiment of an actual object such as a vehicle; a simulation unit that performs a simulation of a collision experiment for the real object by using CAE analysis software based on a finite element method, for a virtual object corresponding to the real object; a mode decomposition unit that performs mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the shape / behavior of the virtual object and a group of simulation curves representing changes in the predetermined locations of the virtual object, including an acceleration output or a displacement output or the like of the predetermined locations; a coefficient calculation unit that compares a group of experimental result curves that represent changes in a predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, included in the experimental result data, with the group of simulation curves, and calculates coefficients that allow the group of simulation curves to fit to the group of experimental result curves; a reconstruction unit that reconstructs the group of simulation curves based on the calculated coefficients and reconstructs virtual shape and behavior data that is expected to have occurred in the crash experiment based on the calculated coefficients; a force output unit that compares the shape / behavior data created by the simulation unit with the shape / behavior data reconstructed by the reconstruction unit, and calculates or estimates a force required to correct the difference; A system to help improve the accuracy of simulation models.

2. A correlation calculation unit is provided for calculating a correlation between the mode decomposed simulation curve group and shape / behavior data and parameters of the virtual object including a plate thickness, The accuracy improvement support system for a simulation model according to claim 1.

3. an experiment result data input unit for inputting experiment result data in a collision experiment of an actual object such as a vehicle; a simulation unit that performs a simulation of a collision experiment for the real object by using CAE analysis software based on a finite element method, for a virtual object corresponding to the real object; a mode decomposition unit that performs mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the shape / behavior of the virtual object and a group of simulation curves representing changes in the predetermined locations of the virtual object, including an acceleration output or a displacement output or the like of the predetermined locations; a difference pattern vector extraction unit that compares a group of experiment result curves that represent changes in a predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, included in the experiment result data, with a group of simulation curves, and extracts a difference pattern vector; a coefficient calculation unit that compares the group of experimental result curves with the group of simulation curves and calculates coefficients for fitting the group of simulation curves to the group of experimental result curves; a reconstruction unit that reconstructs the group of simulation curves based on the calculated coefficients and reconstructs virtual shape and behavior data that is expected to have occurred in the crash experiment based on the calculated coefficients; a force output unit that compares the shape / behavior data created by the simulation unit with the shape / behavior data reconstructed by the reconstruction unit, and calculates or estimates a force required to correct the difference; a knowledge database unit for storing data including past experiment results, past simulation results, past proposal reports, and information regarding differences between past experiment results and simulations; a similarity determination unit that determines a similarity between the current experiment result and a simulation based on the experiment result data, the shape / behavior data created by the simulation unit, the shape / behavior data reconstructed by the reconstruction unit, or data from the knowledge database unit; and a suggestion unit that extracts measures based on data in the knowledge database in cases where there is a high degree of similarity between the experimental results and the simulation results based on an output from the similarity determination unit, and proposes how to change parameters of the virtual object, including plate thickness, etc., to increase a predetermined evaluation score. A system to help improve the accuracy of simulation models.

4. a deviation visualization unit that, based on the shape and behavior data created by the simulation unit and the shape and behavior data reconstructed by the reconstruction unit, vectorizes information on a group of node coordinates in a mode space of a predetermined member in the shape and behavior data, and calculates a distance or direction in the mode space by decomposing the vector into modes, and visualizes the degree of deviation of the shape and behavior of the predetermined member from the direction of the difference pattern vector, whether or not the mode coordinate values ​​are close at a predetermined timing; the suggestion unit extracts measures for cases in which there is a high degree of similarity between the results of the current experiment and the simulation based on an output from at least one of the similarity determination unit and the deviation visualization unit, based on data in the knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. The system for supporting improvement of accuracy of a simulation model according to claim 3.

5. The similarity determination unit is a deviation pattern similarity determination unit that determines a similarity of a deviation pattern of a curve based on the difference pattern vector; a deformation mode similarity determination unit that, based on the shape / behavior data created by the simulation unit and the shape / behavior data reconstructed by the reconstruction unit, vectorizes information on a group of node coordinates in a mode space in the shape / behavior data, and calculates a distance or direction in the mode space by decomposing the vector into modes, and determines a similarity of the deformation modes at a predetermined timing based on whether or not the mode coordinate values ​​are close to each other; the suggestion unit extracts measures for cases in which there is a high degree of similarity between the results of the current experiment and the simulation based on an output from at least one of the deviation pattern similarity determination unit, the deformation mode similarity determination unit, and the deviation visualization unit, based on data from a knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. The system for supporting improvement of accuracy of a simulation model according to claim 4.

6. A correlation calculation unit is provided for calculating a correlation between the mode decomposed simulation curve group and shape / behavior data and parameters of the virtual object including a plate thickness, 6. A system for supporting improvement of accuracy of a simulation model according to claim 3.

7. An automatic execution unit that automatically executes the countermeasure proposed by the suggestion unit.

6. A system for supporting improvement of accuracy of a simulation model according to claim 3.

8. An automatic execution unit that automatically executes the countermeasure proposed by the suggestion unit. The system for supporting improvement of accuracy of a simulation model according to claim 6.

9. inputting, by an experiment result data input unit, experiment result data of a collision experiment of an actual object such as a vehicle; A simulation unit performs a simulation of a collision test for the collision test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; a step of performing mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the shape / behavior of the virtual object and the group of simulation curves representing changes in the predetermined locations of the virtual object, including an acceleration output or a displacement output or the like of the predetermined locations; a coefficient calculation unit comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; a step of reconstructing the group of simulation curves based on the calculated coefficients by a reconstruction unit, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; and a step of comparing the shape / behavior data created by the simulation unit with the shape / behavior data reconstructed by the reconstruction unit, by a force output unit, and calculating or estimating a force required to correct the difference. A method for helping improve the accuracy of simulation models.

10. A correlation calculation unit calculates a correlation between the mode decomposed simulation curve group and shape / behavior data and parameters of the virtual object including a plate thickness, etc. The method for supporting improvement of accuracy of a simulation model according to claim 9.

11. inputting, by an experiment result data input unit, experiment result data of a collision experiment of an actual object such as a vehicle; A simulation unit performs a simulation of a collision test for the collision test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; a step of performing mode decomposition on the shape / behavior data and a group of simulation curves created by the simulation unit, i.e., the shape / behavior data representing the behavior of the virtual object and a group of simulation curves representing changes in the predetermined locations of the virtual object, including an acceleration output or a displacement output or the like of the predetermined locations; a step of comparing a group of experimental result curves, which include an acceleration output or a displacement output or the like of the predetermined portion of the substantial object obtained by the collision experiment and are included in the experimental result data, with the group of simulation curves, and extracting a difference pattern vector; a coefficient calculation unit comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; a step of reconstructing the group of simulation curves based on the calculated coefficients by a reconstruction unit, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; A step of comparing the shape / behavior data created by the simulation unit with the shape / behavior data reconstructed by the reconstruction unit, and calculating or estimating a force required to correct the difference, by a force output unit; a step of determining a similarity regarding the difference between the current experiment result and the simulation by a similarity determination unit based on data from a knowledge database unit that stores the experiment result data, the shape / behavior data created by the simulation unit, the shape / behavior data reconstructed by the reconstruction unit, or past experiment results, past simulation results, past proposal reports, and data including information regarding the difference between past experiment results and simulations; and a step of extracting, by a suggestion unit, measures in cases where there is a high degree of similarity regarding the difference between the current experimental result and the simulation based on the output of the similarity determination unit, based on the data in the knowledge database unit, and proposing how to change the parameters of the virtual object, including plate thickness, etc., in order to increase a predetermined evaluation score. A method for helping improve the accuracy of simulation models.

12. a deviation visualization unit, based on the shape and behavior data created by the simulation unit and the shape and behavior data reconstructed by the reconstruction unit, vectorizing information of a group of node coordinates in a mode space of a predetermined member in the shape and behavior data, and decomposing the vector into modes to calculate a distance or direction in the mode space, and visualizing whether or not the mode coordinate values ​​are close at a predetermined timing and the degree of deviation of the shape and behavior of the predetermined member from the direction of the difference pattern vector; The suggesting step extracts measures for cases where there is a high degree of similarity between the experimental results and the simulation based on the output of at least one of the similarity determining unit and the deviation visualizing unit, based on the data in the knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. The method for supporting improvement of accuracy of a simulation model according to claim 11.

13. The step of determining the degree of similarity comprises: a step of determining a similarity of deviation patterns of curves based on the difference pattern vector by a deviation pattern similarity determining unit included in the similarity determining unit; a deformation mode similarity determination unit provided in the similarity determination unit, based on the shape / behavior data created by the simulation unit and the shape / behavior data reconstructed by the reconstruction unit, vectorizing information of a group of node coordinates in a mode space in the shape / behavior data, and calculating distances in the mode space by decomposing the vectors into modes, and determining the similarity of the deformation modes based on whether or not the mode coordinate values ​​are close at a predetermined timing; The suggesting step extracts measures for cases where there is a high degree of similarity between the experimental results and the simulation based on the output of at least one of the deviation pattern similarity determination unit, the deformation mode similarity determination unit, and the deviation visualization unit, based on data in the knowledge database, and suggests how to change the parameters to increase a predetermined evaluation score. The method for supporting improvement of accuracy of a simulation model according to claim 12.

14. A correlation calculation unit calculates a correlation between the mode decomposed simulation curve group and shape / behavior data and parameters of the virtual object including a plate thickness, etc. The method for supporting improvement of accuracy of a simulation model according to any one of claims 11 to 13.

15. The method further includes a step of automatically executing the countermeasure proposed by the suggestion unit using an automatic execution unit. The method for supporting improvement of accuracy of a simulation model according to any one of claims 11 to 13.

16. The method further includes a step of automatically executing the countermeasure proposed by the suggestion unit using an automatic execution unit. The method for supporting improvement of accuracy of a simulation model according to claim 14.

17. A program for supporting improvement of accuracy of a simulation model, the program being configured to: inputting experimental result data in a collision experiment of a real object such as a vehicle; performing a crash test simulation for the crash test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; A step of performing mode decomposition on shape / behavior data and a group of simulation curves, i.e., shape / behavior data representing a shape / behavior of the virtual object and a group of simulation curves representing changes in a predetermined location of the virtual object, including an acceleration output or a displacement output, etc., of the predetermined location; a step of comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; reconstructing the simulation curves based on the calculated coefficients, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; comparing the shape and behavior data created by the simulation with the reconstructed shape and behavior data, and calculating or estimating a force required to correct the difference; A program to help improve the accuracy of simulation models.

18. The program causes the computer to Executing a step of calculating a correlation between the mode decomposed simulation curve group and shape / behavior data and parameters of the virtual object including a plate thickness, etc. A program for supporting improvement of accuracy of a simulation model according to claim 17.

19. A program for supporting improvement of accuracy of a simulation model, the program being configured to: inputting experimental result data in a collision experiment of a real object such as a vehicle; performing a crash test simulation for the crash test on a virtual object corresponding to the real object by CAE analysis software based on a finite element method; A step of performing mode decomposition on shape / behavior data and a group of simulation curves, i.e., shape / behavior data representing a shape / behavior of the virtual object and a group of simulation curves representing changes in a predetermined location of the virtual object, including an acceleration output or a displacement output, etc., of the predetermined location; a step of comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object obtained by the collision experiment, including an acceleration output or a displacement output or the like of the predetermined location, with the group of simulation curves, and extracting a difference pattern vector; a step of comparing a group of experimental result curves, which are included in the experimental result data and which represent changes in the predetermined location of the substantial object, including an acceleration output or a displacement output or the like of the predetermined location obtained by the collision experiment, with the group of simulation curves, and calculating coefficients for fitting the group of simulation curves to the group of experimental result curves; reconstructing the simulation curves based on the calculated coefficients, and reconstructing virtual shape and behavior data that is expected to have occurred in the crash test based on the calculated coefficients; A step of comparing the shape and behavior data created by the step of performing the simulation with the reconstructed shape and behavior data, and calculating or estimating a force required to correct the difference; a step of determining a degree of similarity regarding the difference between the current experiment result and the simulation based on the data from the knowledge database unit storing the experiment result data, the shape / behavior data created by the step of performing the simulation unit, the shape / behavior data reconstructed by the step of reconstructing, or data including past experiment results, past simulation results, past proposal reports, and information regarding the difference between past experiment results and simulations; and extracting measures based on the data in the knowledge database unit in a case where there is a high degree of similarity between the difference between the current experimental result and the simulation based on an output from the step of determining the degree of similarity, and proposing how to change parameters of the virtual object, including plate thickness, etc., in order to increase a predetermined evaluation score. A program to help improve the accuracy of simulation models.

20. The program causes the computer to a step of vectorizing information of a group of nodal coordinates in a mode space of a predetermined member in the shape / behavior data based on the shape / behavior data created in the step of performing the simulation and the shape / behavior data reconstructed in the step of reconstructing, calculating a distance or direction in the mode space by decomposing the vector into modes, and visualizing whether or not the mode coordinate values ​​are close at a predetermined timing and the degree of deviation of the shape / behavior of the predetermined member from the direction of the difference pattern vector; The proposed steps include: a step of extracting measures based on data from the knowledge database unit in a case where the degree of similarity between the experimental result and the simulation is high, based on an output from at least one of the steps of determining the degree of similarity and visualizing the degree of discrepancy, and proposing how to change the parameters in order to increase a predetermined evaluation score; 20. A program for supporting improvement of accuracy of a simulation model according to claim 19.

21. The program causes the computer to The step of determining the similarity includes: determining a similarity of deviation patterns of the curves based on the difference pattern vector; a step of vectorizing information of a group of node coordinates in a modal space in the shape / behavior data based on the shape / behavior data created in the step of performing the simulation and the shape / behavior data reconstructed in the step of reconstructing, calculating a distance or direction in the modal space by decomposing the vector into modes, and judging a similarity of the deformation modes based on whether the modal coordinate values ​​are close at a predetermined timing, The proposed steps include: extracting measures based on data in the knowledge database in cases where there is a high degree of similarity between the experimental results and the simulation based on output from at least one of the steps of determining the similarity of the deviation patterns, determining the similarity of the deformation modes, and visualizing the deviation, and proposing how to change the parameters to increase a predetermined evaluation score; 21. A program for supporting improvement of accuracy of a simulation model according to claim 20.

22. The program causes the computer to Executing a step of calculating a correlation between the mode decomposed simulation curve group and shape / behavior data and parameters of the virtual object including a plate thickness, etc.

22. A program for supporting improvement of accuracy of a simulation model according to claim 19.

23. The program includes: further executing a step of automatically executing the countermeasure proposed by the proposing step; 22. A program for supporting improvement of accuracy of a simulation model according to claim 19.

24. The program includes: further executing a step of automatically executing the countermeasure proposed by the proposing step; 23. A program for supporting improvement of accuracy of a simulation model according to claim 22.

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