System early-stage scheme refined design and verification method based on total element digital model and prototype

Abstract

The invention provides a system early-stage scheme refined design and verification method based on a total element digital model and a prototype, and relates to the technical field of system modeling simulation. The system early-stage scheme refined design and verification method based on the total-factor digital model and the prototype comprises the following steps: step 1, establishing an equipment demand model; 2, designing a demand model and system simulation model interface; step 3, constructing a system simulation model; step 4, expanding the scheme space; 5, space expansion is designed in detail; and step 6, performing optimization verification. According to the method, based on a total-element digital prototype and scheme design and verification, the original work with verification conditions in a detailed design stage is realized, the work is advanced to the scheme design stage, design defects are found in advance, the original work needs to be carried out through a large number of physical tests in an integrated test stage, and the work needs to be carried out through multiple iterations in digital prototype test verification. And the test reliability and the test efficiency of the physical test are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of system modeling and simulation, and in particular to a method for fine-grained design and verification of early system solutions based on full-element digital models and prototypes. Background Art

[0002] At present, in the discussion and practice of digital system engineering, the mainstream theory is based on model-based system engineering theory, that is, MBSE theory. The main problem that MBSE theory solves is to unify and integrate the traditional models and data sources used independently and the specific models that rely on independent disciplines to form a complete set of system-level models, and push the engineering discipline to a model-based integration method, thereby establishing a digital environment. However, the actual application of MBSE theory in engineering is more in the digital test or theoretical verification stage of the actual system, and cannot directly participate in the design and solution demonstration of engineering equipment.

[0003] The large process of engineering equipment design and verification and system engineering generally include equipment demand analysis, scheme design, detailed design, product manufacturing, product testing, system testing and operation and maintenance. The model in model-based system engineering mainly refers to the demand analysis model or the integrated multidisciplinary system model. The above two types of models alone or in combination cannot directly participate in the system scheme design or detailed design stage and product manufacturing process, resulting in a certain separation between equipment design and system verification, which will increase the overall design cycle and cause repeated modification of the scheme.

[0004] A digital prototype is a digital model of a system or subsystem expressed by a computer in digital form. It can have the same dimensional characteristics and principle characteristics as a real physical product, and can verify the functions and performance of a physical prototype through a digital prototype. In addition to the traditional demand model and system simulation model, the digital prototype also includes a system design model and a product manufacturing model that includes product structure, process design, manufacturing process, and quality management.

[0005] With the design requirements of variable configurations and variable indicators for new and complex engineering equipment, the traditional technical means of limited scheme comparison, limited product genealogy selection, and limited typical working condition verification have become inadequate. It is necessary to expand the threshold and seek optimization within a limited range, generate massive schemes, select massive product components, and conduct sufficient verification on digital models or digital prototypes to seek the optimal solution, product, and finalization.

[0006] Therefore, those skilled in the art provide a method for refined design and verification of early system solutions based on full-factor digital models and prototypes to solve the problems raised in the above background technology. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] In view of the shortcomings of the prior art, the present invention provides a method for refined design and verification of early system solutions based on full-factor digital models and prototypes, and constructs a full-factor digital prototype. Through the digital prototype, detailed design work as well as virtual testing and verification work of parameter engineering equipment can be performed, thereby realizing a fully digital expression of system engineering, and solving the problem that the existing model-based system engineering theory cannot fully cover the system engineering design process of engineering equipment.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] A method for fine-grained design and verification of early system solutions based on full-factor digital models and prototypes, comprising the following steps:

[0012] Step 1. Establish equipment demand model

[0013] Design the demand model according to the functional requirements, mission requirements, performance requirements, interface requirements, test verification requirements and performance test requirements of the physical equipment;

[0014] Step 2. Design of interface between demand model and system simulation model

[0015] Design the association interface or program between the demand model and the system simulation model to achieve a high degree of binding between the basic framework of the system simulation model and the demand model;

[0016] Step 3. System simulation model construction

[0017] The system simulation model is mainly based on the functional model in the digital prototype, and integrates the geometric model, performance model and semi-physical model;

[0018] Step 4. Solution space expansion

[0019] The expansion of the solution space includes the expansion of the design solution and the expansion of the experimental solution;

[0020] Step 5. Detailed design of space expansion

[0021] Detailed design space expansion means that in the process of building and integrating the system simulation model, some specific variables are set in the form of parameters through the basic syntax of the Modelica language, and the model is encapsulated at the outermost layer of the model, and the parameters are exposed at the top level of the model, so that the model users can modify the model parameters at the top level.

[0022] Step 6. Optimize verification

[0023] Optimization verification includes parameter sensitivity analysis and parameter optimization of the system.

[0024] Furthermore, in step 2, the interface design of the requirement model and the system simulation model specifically includes: directly generating a system simulation model framework according to the requirement model established in step 1, such as designing a software program tool, connecting the requirement model designed in the Sysml language and the software supporting Modelica modeling, and realizing the generation of the Modelica model framework and interface settings according to the Sysml requirement model.

[0025] Furthermore, in step 3, the geometric model and the system simulation model are integrated through a CAD interface tool, and the three-dimensional structural model in a format such as .step is exported from the three-dimensional structural design software. The three-dimensional model is split into a multi-body dynamics model including basic mass attributes, position attributes and kinematic pair relationships according to the assembly relationship through the CAD interface tool, so as to realize the system integration of the one-dimensional model or the rigid body mechanics model;

[0026] The performance model and the system simulation model are integrated through model degradation tools or mechanism-data fusion tools. For simulation models of high-order physical fields, the model reduction tool is used to reduce the scale of the equation system to be solved by the model. The three-dimensional CFD model and the finite element model are reduced to a one-dimensional model to reduce the nonlinearity of the model. The reduced-order modules are integrated with the system simulation model.

[0027] For complex system models with unknown mechanisms, the mechanism-data fusion tool is used to train the experimental data to generate a data model, which is then combined with the physical mechanism of the system simulation model to form a mechanism-data fusion model.

[0028] Furthermore, in step 3, data interaction is achieved between the semi-physical model and the system simulation model through a data protocol and a data transmission program.

[0029] Further, in step 4, the design scheme expansion mainly generates different design schemes according to the design requirements of the equipment, generates different equipment product structure trees and different physical appearance characteristics based on different design schemes, and generates a system simulation model family based on the main system simulation model in the same model integration modeling method, wherein the model family includes selectable system simulation models based on different design schemes that can include all physical characteristics and functional characteristics of the equipment;

[0030] The test scheme expansion is mainly based on the test verification requirements in the demand model. The input parameters or input conditions of the specific scheme are processed in batches, and the input parameter sets are combined in batches in the form of uniform, non-uniform, random distribution and normal distribution. The design matrix is ​​generated, and the batch test scheme design is carried out for the system simulation model based on the design matrix.

[0031] Furthermore, in the step 5, it also includes: based on the basic syntax of Modelica, supporting the unified setting of multiple parameters, designing component models for basic components through single and multiple parameter settings, and directly selecting models for basic components of the same type through model codes according to actual parameters;

[0032] Based on this, the component product library of the physical equipment is established and managed at the component level, supporting the selection of different models of products from the product library for specific modeling and optimization of the digital prototype of the equipment.

[0033] Further, in step 6, the parameter sensitivity analysis first selects an input parameter set related to the result variable of interest based on the experience of the designer or tester, sets the input parameter range, for example, for the flight altitude and speed of the aircraft, sets the influence of its fuel deviation, specific impulse deviation, thrust deviation and other parameters on it, the deviation parameter is set to ±50% of the calibration scheme parameter, and the change distribution of the input parameter is set within the range of ±50%, such as uniform distribution, to form an input parameter matrix of 3 input parameters under uniform distribution, and then performs sensitivity analysis on each input parameter combination in the input parameter matrix, and the analysis results are presented in the form of scatter plots and tornado plots;

[0034] Parameter optimization reselects the parameters that have a greater impact on the results in the input parameter set based on the results of parameter sensitivity analysis, sets the value range of each parameter in the new parameter set, and also sets the parameter matrix. Then, the optimization target requirements are selected, such as maximum value maximization requirements, target value overshoot requirements, and final value minimization requirements. Based on the above specific requirements, the optimization of specific demand scenarios for one or more output parameters is completed.

[0035] Further, a system simulation model based on full-factor digital models and prototypes, including demand models, geometric models, functional models, performance models and semi-physical models;

[0036] The requirement model is a design requirement model that includes the functional requirements, mission requirements, performance requirements, interface requirements, test verification requirements, and performance test requirements of physical equipment;

[0037] Geometric model, i.e., geometric CAD model that can reflect the geometric features of physical equipment and finite element CAE model that includes the structural features of physical equipment;

[0038] Functional model, i.e., system design model based on Modelica language including system functions, operation logic and system architecture, as well as system simulation model including multi-disciplinary comprehensive functions, performance and general quality characteristics such as mechanical, electrical, hydraulic, control, thermal and software;

[0039] Performance model, i.e., system simulation model including physical field characteristics such as structural force, thermal and electromagnetic;

[0040] A semi-physical model is a real controller device and a data-calibrated mathematical model connected to the physical controller that can characterize the operating status of the physical device.

[0041] Furthermore, the construction of the system simulation model includes the construction of a digital model and a digital prototype, and its basic framework is based on a system simulation model established based on the Modelica language, in which the demand model, geometric model, performance model and semi-physical model are integrated, and then solved based on a unified solver;

[0042] The simulation model framework based on the demand model is directly generated through professional interfaces or integration methods between the demand model and the system simulation model, thereby realizing the integration between the demand model and the system simulation model.

[0043] (III) Beneficial effects

[0044] The present invention provides a method for fine-grained design and verification of early system solutions based on full-factor digital models and prototypes. It has the following beneficial effects:

[0045] 1. The present invention provides a method for refined design and verification of early system solutions based on full-factor digital models and prototypes. Based on full-factor digital prototypes and solution design and verification, the work that originally had verification conditions in the detailed design stage can be advanced to the solution design stage, thereby discovering design defects early.

[0046] 2. The present invention provides a method for refined design and verification of early system solutions based on full-factor digital models and prototypes. The work that originally needed to be carried out through a large number of physical tests in the integration test phase is iterated multiple times in the digital prototype test verification, thereby improving the test reliability and test efficiency of the physical test.

[0047] 3. The present invention provides a method for refined design and verification of early system solutions based on full-factor digital models and prototypes, constructing a full-factor digital prototype so that detailed design work of parameter engineering equipment as well as virtual testing and verification work can be performed through the digital prototype, thereby realizing full digital expression of system engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1A flowchart for building a digital prototype and designing and verifying a scheme for the present invention;

[0049] Figure 2 A schematic diagram of constructing a system simulation model of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the specific embodiments of the present invention to clearly and completely describe the technical solutions in the specific embodiments of the present invention. Obviously, the specific embodiments described are only part of the specific embodiments of the present invention, not all of the specific embodiments. Based on the specific embodiments of the present invention, all other specific embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific implementation method:

[0052] like Figure 1-2 As shown, a specific embodiment of the present invention provides a system early solution refined design and verification method based on a full-factor digital model and a prototype, comprising the following steps:

[0053] Step 1. Establish equipment demand model

[0054] Design the demand model according to the functional requirements, mission requirements, performance requirements, interface requirements, test verification requirements and performance test requirements of the physical equipment;

[0055] Step 2. Design of interface between demand model and system simulation model

[0056] Design the associated interface or program between the demand model and the system simulation model to achieve a high degree of binding between the basic framework of the system simulation model and the demand model. Directly generate the system simulation model framework according to the demand model established in step 1. For example, design a software program tool to connect the demand model designed in Sysml language with the software supporting Modelica modeling, so as to generate the Modelica model framework and interface settings according to the Sysml demand model.

[0057] Step 3. System simulation model construction

[0058] The system simulation model is mainly based on the functional model in the digital prototype, and integrates the geometric model, performance model and semi-physical model, such as Figure 2 As shown;

[0059] The geometric model and the system simulation model are integrated through the CAD interface tool. The 3D structural model in the .step format is exported from the 3D structural design software. The CAD interface tool is used to split the 3D model into a multi-body dynamics model including basic mass attributes, position attributes and kinematic pair relationships according to the assembly relationship, thus realizing the system integration of the one-dimensional model or rigid body mechanics model.

[0060] The performance model and the system simulation model are integrated through model degradation tools or mechanism-data fusion tools. For simulation models of high-order physical fields, the model reduction tool is used to reduce the scale of the equation system to be solved by the model. The three-dimensional CFD model and the finite element model are reduced to a one-dimensional model to reduce the nonlinearity of the model. The reduced-order modules are integrated with the system simulation model.

[0061] For complex system models with unknown mechanisms, the mechanism-data fusion tool is used to train the test data to generate a data model, which is then combined with the physical mechanism of the system simulation model to form a mechanism-data fusion model;

[0062] Data interaction between the semi-physical model and the system simulation model is achieved through data protocols and data transmission programs;

[0063] Step 4. Solution space expansion

[0064] The expansion of the solution space includes the expansion of the design solution and the expansion of the experimental solution;

[0065] Design scheme expansion mainly generates different design schemes based on the design requirements of the equipment, generates different equipment product structure trees and different physical appearance features based on different design schemes, and generates a system simulation model family based on the main system simulation model in the same model integration modeling method. The model family contains selectable system simulation models based on different design schemes that can include all physical and functional characteristics of the equipment;

[0066] The test scheme expansion is mainly based on the test verification requirements in the demand model. The input parameters or input conditions of a specific scheme are processed in batches, and the input parameter sets are combined in batches in the form of uniform, non-uniform, random distribution and normal distribution, and a design matrix is ​​generated. Based on the design matrix, batch test scheme design is performed for the system simulation model.

[0067] Step 5. Detailed design of space expansion

[0068] Detailed design space expansion means that in the process of building and integrating the system simulation model, some specific variables are set in the form of parameters through the basic syntax of Modelica language, and the model is encapsulated at the outermost layer of the model, and the parameters are exposed at the top layer of the model, so that the model user can modify the model parameters at the top layer. At the same time, based on the basic syntax of Modelica, it supports the unified setting of multiple parameters, and the component model design of basic components is carried out through the single and multiple setting methods of parameters. For basic components of the same type, the model selection is directly carried out through the model code according to the actual parameters;

[0069] Based on this, the component product library of the physical equipment is established and managed at the component level, supporting the selection of different models of products from the product library for specific modeling and optimization of the digital prototype of the equipment.

[0070] Step 6. Optimize verification

[0071] Optimization verification includes parameter sensitivity analysis and parameter optimization of the system;

[0072] Parameter sensitivity analysis first selects the input parameter set related to the result variable of interest based on the experience of the designer or tester, and sets the input parameter range. For example, for the flight altitude and speed of the aircraft, set the influence of its fuel deviation, specific impulse deviation, thrust deviation and other parameters on it. The deviation parameter is set to ±50% of the calibration scheme parameter. Within the range of ±50%, the change distribution of the input parameter is set, such as uniform distribution, to form an input parameter matrix of 3 input parameters under uniform distribution. Then, sensitivity analysis is performed on each input parameter combination in the input parameter matrix, and the analysis results are presented in the form of scatter plots and tornado plots.

[0073] Parameter optimization reselects the parameters that have a greater impact on the results in the input parameter set based on the results of parameter sensitivity analysis, sets the value range of each parameter in the new parameter set, and also sets the parameter matrix. Then, the optimization target requirements are selected, such as maximum value maximization requirements, target value overshoot requirements, and final value minimization requirements. Based on the above specific requirements, the optimization of specific demand scenarios for one or more output parameters is completed.

[0074] The system simulation model based on full-factor digital models and prototypes includes demand models, geometric models, functional models, performance models and semi-physical models;

[0075] The requirement model is a design requirement model that includes the functional requirements, mission requirements, performance requirements, interface requirements, test verification requirements, and performance test requirements of physical equipment;

[0076] Geometric model, i.e., geometric CAD model that can reflect the geometric features of physical equipment and finite element CAE model that includes the structural features of physical equipment;

[0077] Functional model, i.e., system design model based on Modelica language including system functions, operation logic and system architecture, as well as system simulation model including multi-disciplinary comprehensive functions, performance and general quality characteristics such as mechanical, electrical, hydraulic, control, thermal and software;

[0078] Performance model, i.e., system simulation model including physical field characteristics such as structural force, thermal and electromagnetic;

[0079] Semi-physical model, i.e., the real controller device and the data-calibrated mathematical model connected to the physical controller that can characterize the operating status of the physical device;

[0080] The construction of the system simulation model includes the construction of a digital model and a digital prototype, and its basic framework is based on a system simulation model established based on the Modelica language, in which the demand model, geometric model, performance model and semi-physical model are integrated, and then solved based on a unified solver;

[0081] The simulation model framework based on the demand model is directly generated through professional interfaces or integration methods between the demand model and the system simulation model, thereby realizing the integration between the demand model and the system simulation model.

[0082] In this application, based on the digital prototype and scheme design and verification of all elements, the work that originally had verification conditions in the detailed design stage was achieved and brought forward to the scheme design stage, so that design defects can be discovered early.

[0083] In this application, the work that originally needed to be carried out through a large number of physical tests in the integration test phase was iterated multiple times in the digital prototype test verification to improve the test reliability and test efficiency of the physical tests.

[0084] In this application, a full-factor digital prototype is constructed so that detailed design work of parameter engineering equipment as well as virtual testing and verification work can be performed through the digital prototype, thereby realizing a fully digital expression of system engineering.

[0085] Although specific embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the specific embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system early solution refinement design and verification method based on full-factor digital models and prototypes, characterized in that: The following steps are involved: Step 1. Establish equipment demand model Design the demand model according to the functional requirements, mission requirements, performance requirements, interface requirements, test verification requirements and performance test requirements of the physical equipment; Step 2. Design of interface between demand model and system simulation model Design the association interface or program between the demand model and the system simulation model to achieve a high degree of binding between the basic framework of the system simulation model and the demand model; Step 3. System simulation model construction The system simulation model is mainly based on the functional model in the digital prototype, and integrates the geometric model, performance model and semi-physical model; Step 4. Solution space expansion The expansion of the solution space includes the expansion of the design solution and the expansion of the experimental solution; Step 5. Detailed design of space expansion Detailed design space expansion means that in the process of building and integrating the system simulation model, some specific variables are set in the form of parameters through the basic syntax of the Modelica language, and the model is encapsulated at the outermost layer of the model, and the parameters are exposed at the top level of the model, so that the model users can modify the model parameters at the top level. Step 6. Optimize verification Optimization verification includes parameter sensitivity analysis and parameter optimization of the system.

2. The method for refined design and verification of early system solutions based on full-factor digital models and prototypes according to claim 1, characterized in that: In step 2, the interface design between the demand model and the system simulation model specifically includes: directly generating a system simulation model framework according to the demand model established in step 1, such as designing a software program tool, connecting the demand model designed in the Sysml language and the software supporting Modelica modeling, and realizing the generation of the Modelica model framework and interface settings according to the Sysml demand model.

3. The method for refined design and verification of early system solutions based on full-factor digital models and prototypes according to claim 1 is characterized in that: In step 3, the geometric model and the system simulation model are integrated through a CAD interface tool, and the three-dimensional structural model in a format such as .step is exported from the three-dimensional structural design software. The three-dimensional model is split into a multi-body dynamics model including basic mass attributes, position attributes and kinematic pair relationships according to the assembly relationship through the CAD interface tool, so as to realize the system integration of the one-dimensional model or the rigid body mechanics model; The performance model and the system simulation model are integrated through model degradation tools or mechanism-data fusion tools. For simulation models of high-order physical fields, the model reduction tool is used to reduce the scale of the equation system to be solved by the model. The three-dimensional CFD model and the finite element model are reduced to a one-dimensional model to reduce the nonlinearity of the model. The reduced-order modules are integrated with the system simulation model. For complex system models with unknown mechanisms, the mechanism-data fusion tool is used to train the experimental data to generate a data model, which is then combined with the physical mechanism of the system simulation model to form a mechanism-data fusion model.

4. The method for refined design and verification of early system solutions based on full-factor digital models and prototypes according to claim 1 is characterized in that: In step 3, data interaction is achieved between the semi-physical model and the system simulation model through a data protocol and a data transmission program.

5. The method for refined design and verification of early system solutions based on full-factor digital models and prototypes according to claim 1 is characterized in that: In step 4, the design scheme expansion mainly generates different design schemes according to the design requirements of the equipment, generates different equipment product structure trees and different physical appearance characteristics based on different design schemes, and generates a system simulation model family based on the main system simulation model in the same model integration modeling method, wherein the model family includes selectable system simulation models based on different design schemes that can include all physical and functional characteristics of the equipment; The test scheme expansion is mainly based on the test verification requirements in the demand model. The input parameters or input conditions of the specific scheme are processed in batches, and the input parameter sets are combined in batches in the form of uniform, non-uniform, random distribution and normal distribution. The design matrix is ​​generated, and the batch test scheme design is carried out for the system simulation model based on the design matrix.

6. The method for refined design and verification of early system solutions based on full-factor digital models and prototypes according to claim 1 is characterized in that: In the step 5, it also includes: based on the basic syntax of Modelica, supporting the unified setting of multiple parameters, designing component models for basic components through single and multiple parameter settings, and directly selecting models for basic components of the same type through model codes according to actual parameters; Based on this, the component product library of the physical equipment is established and managed at the component level, supporting the selection of different models of products from the product library for specific modeling and optimization of the digital prototype of the equipment.

7. The method for refined design and verification of early system solutions based on full-factor digital models and prototypes according to claim 1 is characterized in that: In step 6, the parameter sensitivity analysis first selects the input parameter set related to the result variable of interest based on the experience of the designer or tester, sets the input parameter range, for example, for the flight altitude and speed of the aircraft, sets the influence of its fuel deviation, specific impulse deviation, thrust deviation and other parameters on it, the deviation parameter is set to ±50% of the calibration scheme parameter, and the change distribution of the input parameter is set within the range of ±50%, such as uniform distribution, to form an input parameter matrix of 3 input parameters under uniform distribution, and then performs sensitivity analysis on each input parameter combination in the input parameter matrix, and the analysis results are presented in the form of scatter plots and tornado plots; Parameter optimization reselects the parameters that have a greater impact on the results in the input parameter set based on the results of parameter sensitivity analysis, sets the value range of each parameter in the new parameter set, and also sets the parameter matrix. Then, the optimization target requirements are selected, such as maximum value maximization requirements, target value overshoot requirements, and final value minimization requirements. Based on the above specific requirements, the optimization of specific demand scenarios for one or more output parameters is completed.

8. A system simulation model based on a full-factor digital model and a prototype, characterized in that: Including demand model, geometric model, functional model, performance model and semi-physical model; The requirement model is a design requirement model that includes the functional requirements, mission requirements, performance requirements, interface requirements, test verification requirements, and performance test requirements of physical equipment; Geometric model, i.e., geometric CAD model that can reflect the geometric features of physical equipment and finite element CAE model that includes the structural features of physical equipment; Functional model, i.e., system design model based on Modelica language including system functions, operation logic and system architecture, as well as system simulation model including multi-disciplinary comprehensive functions, performance and general quality characteristics such as mechanical, electrical, hydraulic, control, thermal and software; Performance model, i.e., system simulation model including physical field characteristics such as structural force, thermal and electromagnetic; A semi-physical model is a real controller device and a data-calibrated mathematical model connected to the physical controller that can characterize the operating status of the physical device.

9. A system simulation model based on a full-factor digital model and a prototype according to claim 8, characterized in that: The construction of the system simulation model includes the construction of a digital model and a digital prototype, and its basic framework is based on a system simulation model established based on the Modelica language, in which the demand model, geometric model, performance model and semi-physical model are integrated, and then solved based on a unified solver; The simulation model framework based on the demand model is directly generated through professional interfaces or integration methods between the demand model and the system simulation model, thereby realizing the integration between the demand model and the system simulation model.

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