Model-based integrated design and verification method for complex equipment systems

Abstract

The present invention belongs to the field of complex equipment system design and verification. It discloses a model-based integrated design and verification method for complex equipment systems. With the four levels of "demand-behavior-structure-parameter" as the framework, combined with mission scenario requirements, a full-process design and analysis method for reusable space vehicle systems is constructed; starting from the demand analysis, the complex system is decomposed, and the model is calibrated and performance verified by combining theoretical and experimental data to construct a reusable space vehicle system model, and carry out system comprehensive analysis and verification; combined with the model's three-dimensional virtual environment and configuration optimization algorithm, various configurations of the space vehicle are quickly optimized to evaluate the feasibility and performance of the design scheme; through the two-way interaction of design and verification models, and the integration of system-level design and professional-level design, the problems of design and verification separation and insufficient model collaboration in the existing technology are solved, and the efficiency and accuracy of complex equipment system design and verification are improved.

Description

Technical Field

[0001] The present invention relates to the field of complex equipment system design and verification, and in particular to a model-based integrated method for complex equipment system design and verification. Background Art

[0002] With the continuous advancement of science and technology, complex equipment systems are increasingly being used in fields such as aerospace, energy, and transportation. These systems are often highly complex and multidisciplinary, involving multiple disciplines such as mechanics, electronics, software, and control. In traditional R&D models, design and verification are often separate processes, lacking effective coordination mechanisms, resulting in long R&D cycles, high costs, and low efficiency.

[0003] The current research and development of complex equipment systems mainly faces the following problems:

[0004] 1) Lack of two-way interaction between design models and verification models: Design models and verification models are usually created by different teams or tools, making data and information difficult to share. As a result, design modifications cannot be reflected in the verification model in a timely manner, and verification results cannot be effectively fed back into the design, affecting design optimization and improvement.

[0005] 2) Disconnect between system-level design and specialized design: System-level design focuses on overall architecture and functional requirements, while specialized design delves deeper into specific components and subsystems. Due to a lack of a consistent communication mechanism, the intent and requirements of system-level design are difficult to promptly convey to specialized-level design, and the results of specialized-level design are also difficult to effectively integrate into system-level design, resulting in insufficient overall performance optimization.

[0006] 3) Difficulties in multidisciplinary collaboration: Complex equipment systems involve multiple disciplines. The models and data formats among different disciplines are not unified, and there is a lack of a unified collaborative platform, which leads to information silos and communication barriers, affecting R&D efficiency and product quality.

[0007] To address these issues, the industry has begun exploring the Model-Based Systems Engineering (MBSE) approach, which establishes a unified system model throughout the entire lifecycle of requirements analysis, design, verification, and maintenance. However, existing MBSE approaches still have some shortcomings in practice:

[0008] 1) Low model integration: Although a system model has been established, the design model and the verification model are still separated, and real-time two-way interaction cannot be achieved, which limits the effectiveness and timeliness of the model.

[0009] 2) Lack of process coordination: The design and verification process does not form a closed loop. Verification after design modifications requires rebuilding the verification model, which consumes a lot of time and resources.

[0010] 3) Imperfect tool chain: Different professional fields use different modeling and simulation tools, making it difficult to achieve unified management and exchange of models and data.

[0011] Therefore, there is an urgent need for an innovative method and process to achieve two-way interaction between design models and verification models, as well as the integration of system-level design and professional-level design. Summary of the Invention

[0012] In view of the above defects or improvement needs of the prior art, the present invention provides a.

[0013] To achieve the above objectives, according to one aspect of the present invention, a model-based integrated method for complex equipment system design and verification is provided, comprising the following steps:

[0014] S1 multi-scenario demand analysis and solution design:

[0015] Based on the four-level framework of "demand-behavior-structure-parameters", combined with the mission scenario requirements, special design and analysis are carried out to build a full-process design and analysis method for reusable space vehicle systems;

[0016] S2 multi-domain system modeling and comprehensive verification:

[0017] Based on the full-process design and analysis method of step S1, starting from the demand analysis, the complex system is decomposed to clarify the granularity and technical focus of the modeling. Based on the system decomposition results, the component model, subsystem model and system model are developed from the bottom up. The model parameters are calibrated and the performance is verified by combining theoretical and experimental data. The reusable space vehicle system model is constructed, and comprehensive system analysis and verification are carried out.

[0018] S3 multi-dimensional immersive experience and evaluation optimization:

[0019] Based on the reusable space launch vehicle system model constructed in step S2, the three-dimensional virtual environment of the model and the configuration optimization algorithm are combined to quickly optimize the various configurations of the space launch vehicle and evaluate the feasibility and performance of the design scheme.

[0020] Preferably, step S1 specifically includes the following steps:

[0021] S11 determines mission requirements and design goals:

[0022] Based on the task scenario, key requirements are extracted and gradually decomposed to the system and subsystem levels to form a systematic design goal. SysML use case diagrams are used to clarify the main functions in the task scenario and the interaction between the system and external objects. The global requirements are then hierarchically decomposed through SysML requirement diagrams to extract the key functions and performance requirements of the system and define the division of tasks for each subsystem.

[0023] S12 system function analysis and dynamic behavior modeling:

[0024] Through dynamic behavior modeling, the key system functions extracted in step S11 are decomposed into multiple specific task activities. SysML activity diagrams are used to describe the dynamic behavior and interaction processes between functional modules, clarifying the execution order and logical relationships of tasks. For each subsystem, a state machine diagram is established to analyze its working mode and state transitions in different task scenarios, and define the trigger conditions and transition paths between states. Through the combination of activity diagrams and state machine diagrams, the dynamic response and functional implementation methods of the system in the time dimension are comprehensively analyzed, and the collaborative work of various functional modules is verified to ensure that the system can operate efficiently under different task requirements.

[0025] S13 system architecture design and model construction:

[0026] Starting from the global functions, the modular composition of the rocket system is clarified. The functional division of labor and interface relationships between modules are defined through SysML BDD diagrams. SysML IBD diagrams are used to describe in detail the connection relationships between the components within the subsystem. Through a unified data interface and simulation platform, the product topology is combined with the professional profile architecture to ensure consistency from top-level design to detailed implementation.

[0027] S14 parameter definition and system verification:

[0028] Through the definition and simulation verification of design parameters, the system performance is ensured to meet the mission requirements.

[0029] Preferably, the verification method includes static indicator verification and dynamic performance verification. The static indicator verification is to verify the static performance of the system using SysML parameter diagrams; the dynamic performance verification is to verify the performance of the rocket in different mission scenarios in combination with dynamic simulation models.

[0030] Preferably, step S2 specifically includes the following steps:

[0031] S21 system breakdown:

[0032] Based on mission requirements, the system functions are decomposed layer by layer into several subsystems, components, and assemblies until they can be directly described through physical models and modeling languages. The reusable space vehicle is decomposed into several subsystems, and then further decomposed into fuel management modules and attitude adjustment modules. During the decomposition process, the granularity of each model and its interface properties are clarified to ensure the consistency of parameter transfer and interaction between components.

[0033] S22 model development and testing:

[0034] Based on the system decomposition results of step S21, a basic component model is developed using the Modelica language. The verified component models are integrated into a subsystem model to ensure the accuracy of the logical and physical relationships between the components within the subsystem. Combining theoretical analysis with experimental data, the key parameters of the component and subsystem models are calibrated. Model accuracy is improved through iterative optimization, and the test results cover both static and dynamic performance.

[0035] S23 system model development and integration:

[0036] Based on system requirements and topology, component and subsystem models are integrated into a complete system model using drag-and-drop modeling tools. The complete system model is verified in multiple scenarios through the MWORKS simulation platform, and the coupling performance between subsystems is verified through multi-domain simulation analysis.

[0037] Preferably, step S3 specifically includes the following steps:

[0038] S31 3D modeling and virtual environment creation:

[0039] By building a three-dimensional model of the space vehicle through a virtual simulation platform and simulating different launch and recovery scenarios, designers can observe the space vehicle's operational performance in different scenarios and optimize the design through virtual scenario simulation.

[0040] S32 configuration optimization and evaluation:

[0041] By combining the model's stage ratio optimization algorithm and the model-based program angle optimization algorithm, various configurations of the space vehicle can be quickly optimized to improve the performance of the space vehicle.

[0042] The inter-stage ratio optimization algorithm of the model specifically includes the following steps:

[0043] (1) Cost model establishment

[0044] The speed increment model is used to evaluate the speed increment required for the flight mission, thereby calculating the fuel consumption required to perform the mission, the speed increment ΔV mission The calculation formula is:

[0045] ΔV mission =ΔV orbit +ΔV g +ΔV d +ΔV p +ΔV r +ΔV margin

[0046] Where: ΔV orbit is the orbital velocity, ΔV r is the relative velocity correction introduced by the Earth's rotation, ΔV is the aerodynamic loss, and ΔVd is the thrust loss, ΔV p is the gravity loss, ΔV margin is the safety margin;

[0047] (2) Parameter definition and modeling

[0048] The rocket system is divided into multiple stages, and the mass composition of each stage includes payload, fuel mass, structural mass, etc. Specifically, in the i-th rocket, define m p,i is the mass of propellant, which consists of fuel and oxidizer and is used to generate thrust; define m e,i is the structural mass; define m f,i The fuel that is not completely consumed after the rocket burns; define the mass ratio n i and the structural coefficient ∈ i The calculation formula is:

[0049]

[0050] (3) Optimization objectives and constraints

[0051] By CER=aM x is associated with the cost, where CER is the cost estimation relationship, x is the cost coefficient, M is the takeoff mass, a is the weighting coefficient, and the optimization objective is defined as minimizing the total takeoff mass GLOW of the rocket, which is calculated as follows:

[0052] min GLOW=m 0,1 +m e,b +m p,b

[0053] Among them, m 0,1 is the total mass of the second-stage rocket; m e,b is the mass of the first-stage rocket structure; m p,b is the mass of the first-stage rocket propellant;

[0054] Taking the Long March 5 as an example, for the two-stage reusable carrier rocket, the constraints are further defined: n i ≥1, 1.2≤TWR1≤2,0.5≤TWR i ≤3, i=2,…,N;

[0055] in, is the number of first-stage engines, TWR1 is the first thrust-to-weight ratio, TWR i is the i-th thrust-to-weight ratio.

[0056] (4) Problem solving and optimization

[0057] The optimization objectives and constraints are organized into a mixed integer nonlinear optimization problem. The velocity increment is calculated using the Tsiolkovsky equation to balance thrust loss and path deviation. The gravity loss ΔV during propulsion is calculated using integrals. g and path correction ΔV p,steering :

[0058]

[0059] Among them, t0 is the starting time, t f is the termination time, g is the acceleration due to gravity, γ is the flight path angle, T is the engine thrust, α is the angle of attack, and m is the rocket mass;

[0060] (5) Result output

[0061] The optimization results in the mass distribution, structural coefficient and thrust-to-weight ratio configuration among various stages, ultimately minimizing the total mass of the rocket system while meeting the ΔV requirements and performance indicators of the flight mission.

[0062] Preferably, for a two-stage reusable launch vehicle, the constraint condition is further defined: n i ≥1, 1.2≤TWR1≤2,0.5≤TWR i ≤3, i=2,…,N;

[0063] in, is the number of first-stage engines, TWR1 is the first thrust-to-weight ratio, TWR i is the i-th thrust-to-weight ratio.

[0064] The program angle optimization algorithm of the model specifically includes the following steps:

[0065] (1) Optimization goal

[0066] The optimization objective is defined as minimizing the rocket fuel consumption z, which is calculated as:

[0067] z=min f

[0068] Where, f is fuel consumption;

[0069] (2) State variables and control variables

[0070] State variables Including horizontal position x, vertical position y, angle θ, horizontal speed Vertical speed Angular velocity and mass m;

[0071] Control variable u = [F, α], where F is thrust and α is angle of attack;

[0072] (3) System constraints

[0073] Let I be the moment of inertia of the rocket, h be the height of the rocket, and κ be the fuel consumption coefficient per unit impulse. Establish the dynamic differential equation constraint of the rocket to describe the motion of the rocket in the world coordinate system, including the changes in position, angle, and velocity. The formula is:

[0074]

[0075] (4) Initial and terminal constraints

[0076] Define the initial state and terminal state, where the initial state is the initial condition X0 of the launch point; the terminal state is the orbital element or landing condition X T ;

[0077] (5) Path and inequality constraints

[0078] For a two-stage reusable launch vehicle: thrust constraint F k :0 <F k <825500×9;

[0079] Angle constraint α k :

[0080] Dynamic load q and axial overload n x :q≤q max ,n x ≤n xmax ;

[0081] Where k is the interpolation number, q max is the maximum dynamic pressure of the integrated vehicle, n xmax It is the maximum axial overload of the integrated carrier;

[0082] (6) Problem Discretization

[0083] The Legendre-Gauss-Lobatto pseudospectral method is used to discretize state variables and control variables into multiple time nodes. The state and control variables are fitted using the Lagrange interpolation function, and the differential equations are converted into algebraic equations to simplify the solution process.

[0084] Discrete form:

[0085]

[0086] Among them, X represents the state variable; X(T) represents the state variable at the node at time T, X i represents the i-th element of the state vector X, Li (T) is the basis function of the Lagrange interpolation function;

[0087] The dynamic equations are solved at discrete nodes:

[0088]

[0089] Among them, T K represents the time K, Indicates time T K The derivative of the state variable, Represents the derivative of the state variable at the discrete node, the differential matrix D Ki Represents the differential value of each basis function of the Lagrange interpolation function at each LGL point;

[0090] (7) Discretization of constraints

[0091] Convert initial conditions, terminal conditions, path constraints and inequality constraints into algebraic constraint relations on discrete points; initial condition X0 and terminal condition X T for:

[0092]

[0093] Where x is used to specify the horizontal displacement of the landing point from the initial point, which can be determined in combination with the location of the recovery station. The inequality constraint is discretized as follows: the thrust limit is discretized F k for:

[0094] 0 <F k <825500×9,k=0,1,...,N

[0095] Angle-limited discrete α k for:

[0096]

[0097] Where N is the number of matching points;

[0098] (8) Problem Solving

[0099] The optimization problem is handed over to the solver for linearization and solution to obtain the optimal fuel consumption, thrust and program angle.

[0100] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0101] 1. The dual-architecture design concept proposed in this paper addresses the difficulty in balancing global functionality and specialized performance in traditional designs by combining architectural design from a product topology perspective with architectural design from a specialized profile perspective. The product topology perspective focuses on system-level functional implementation and modular design, ensuring the coordinated operation of various subsystems, while the specialized profile perspective deeply optimizes the technical details of each subsystem, ensuring the depth and precision of technical implementation. This combination of the two organically integrates the system's global functionality with specialized technologies, improving design efficiency and precision.

[0102] 2. By introducing bidirectional interaction between design and verification models, the traditional separation between design and verification is resolved. Models generated during the design phase interact smoothly with verification models, and verification results are fed back to the design phase in real time, driving continuous design optimization. This closed-loop mechanism seamlessly connects the design and verification processes, avoiding information loss and duplicate modeling, significantly improving the efficiency and accuracy of both design and verification.

[0103] 3. By integrating system-level design with specialized design, this invention ensures high consistency from requirements definition to subsystem design. A unified modeling language and interface protocol allow timely feedback from each subsystem design to be transmitted to the system-level design, ensuring that all aspects of the design process work together. This real-time feedback mechanism enables optimization and adjustment of design solutions during the verification phase, enhancing design flexibility and adaptability and ensuring optimal overall system performance.

[0104] In summary, through the above technical solutions, the present invention effectively addresses existing issues such as the separation of design and verification, insufficient interdisciplinary collaboration, and slow optimization processes. It promotes the digitalization and automation of complex equipment system design and verification, and improves the overall efficiency and accuracy of equipment research and development. Applied to the design and verification of complex engineering systems, the present invention provides important technical support for the efficient development of complex equipment systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 It is the overall framework diagram of system design and verification integration.

[0106] Figure 2 It is the overall design idea of ​​multi-scenario demand analysis and solution design.

[0107] Figure 3 It is a decomposition flow chart of multi-domain system modeling and comprehensive verification system.

[0108] Figure 4 It is a flowchart for the development of multi-domain system modeling and comprehensive verification components.

[0109] Figure 5 It is an integrated process of multi-domain system modeling and comprehensive verification system model.

[0110] Figure 6 It is an overall framework for multi-dimensional immersive experience and evaluation optimization. DETAILED DESCRIPTION

[0111] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0112] See also Figure 1 and Figure 6 The present invention provides a model-based integrated method for complex equipment system design and verification, comprising the following steps:

[0113] S1 multi-scenario demand analysis and solution design:

[0114] Based on the concepts of MBSE and SysML, with the four-level framework of "requirements-behavior-structure-parameters" and combined with mission scenario requirements, we conduct special design and analysis to build a full-process design and analysis method for reusable space vehicle systems. The specific implementation method is as follows:

[0115] S11 determines mission requirements and design goals:

[0116] The design work is based on mission scenarios, extracting key requirements and gradually breaking them down to the system and subsystem levels to form systematic design goals: Mission Analysis and Use Case Definition: Based on the typical requirements of space vehicles in missions such as launch, orbit adjustment, and recovery, SysML use case diagrams are used to clarify the main functions in the mission scenarios and the interactions between the system and external objects. For example, the requirements for the recovery of a first-stage rocket include deceleration, stable landing, and post-recovery status assessment. Requirements Decomposition and Correlation Analysis: Using SysML requirement diagrams, global requirements are hierarchically decomposed to extract key system functions and performance requirements, and define the division of labor for each subsystem. For example, the propulsion system must meet the requirements of controllable thrust output, and the thermal control system must ensure stable temperature regulation.

[0117] S12 system function analysis and dynamic behavior modeling:

[0118] Through dynamic behavior modeling, the system's dynamic response and functional implementation are analyzed to ensure that the system meets mission requirements. Functional decomposition and dynamic analysis: System functions are decomposed into multiple task activities, and the dynamic behavior of each functional module is modeled using SysML activity diagrams. For example, the rocket recovery process includes activities such as trajectory adjustment, deceleration, and landing cushioning, with input and output parameters defined for each stage. Subsystem state description: For each subsystem, a state machine diagram is created to clarify its operating mode and state transitions in different mission scenarios. For example, the propulsion system's states may include ignition, thrust adjustment, and shutdown, and the state transition conditions are defined based on the mission requirements.

[0119] S13 system architecture design and model construction:

[0120] The system architecture design is completed by combining the product topology perspective with the professional profile perspective: Product topology perspective architecture: Starting from the global function, clarify the modular composition of the rocket system, such as the core first-stage system, the core second-stage system, the thermal control system, etc. The functional division of labor and interface relationship between modules are defined through SysML BDD diagrams to ensure that the modules work together; Professional profile perspective architecture: In-depth analysis of the technical requirements and implementation methods within the subsystem, using SysML BDD diagrams to describe in detail the connection relationship between the components within the subsystem. For example, the collaborative interface between the attitude adjustment module of the control system and the propulsion system realizes feedback control through data transmission; Comprehensive optimization of the architectural level: Through a unified data interface and simulation platform, the product topology and professional profile architecture are combined to ensure consistency from top-level design to detailed implementation.

[0121] S14 parameter definition and system verification:

[0122] Through the definition and simulation verification of design parameters, ensure that the system performance meets the mission requirements: Index design: Based on the mission requirements and behavioral modeling results, formulate the key performance indicators of the system, such as total mass, thrust range, trajectory accuracy, etc.; Static indicator verification: Use SysML parameter diagrams to verify the static performance of the system, such as the strength analysis of the rocket body structure under different load conditions; Dynamic performance verification: Combined with dynamic simulation models, verify the performance of the rocket in different mission scenarios, such as the accuracy of trajectory adjustment and the response time of the propulsion system.

[0123] S2 multi-domain system modeling and comprehensive verification:

[0124] Based on the full-process design and analysis method of step S1, starting from the demand analysis, the complex system is decomposed to clarify the granularity and technical focus of the modeling. Based on the system decomposition results, the component model, subsystem model and system model are developed from the bottom up. The model parameters are calibrated and the performance is verified by combining theoretical and experimental data. The reusable space vehicle system model is constructed, and comprehensive system analysis and verification are carried out.

[0125] This method follows the model-based system engineering concept and is divided into three stages: System decomposition stage: Starting from demand analysis, the complex system is decomposed into several subsystems, and further refined into components and assemblies, clarifying the granularity of modeling and the technical focus, see Figure 2 Model development and testing phase: Based on the system decomposition results, develop component models, subsystem models and system models from bottom to top, and calibrate the parameters and verify the performance of the models by combining theoretical and experimental data. Figure 3 、 Figure 5 System model development phase: Based on the requirements and business scenarios, the component model and subsystem model are integrated based on the system topology structure to build a complete system model and conduct comprehensive performance verification. Figure 4 .

[0126] The specific steps include:

[0127] S21 system breakdown:

[0128] System decomposition is the foundation for modeling complex systems. Based on mission requirements, system functions are decomposed layer by layer into several subsystems, components, and assemblies until they can be directly described through physical models and modeling languages. Top-down functional decomposition: The reusable space vehicle is decomposed into subsystems such as the propulsion system, control system, and thermal control system, and further decomposed into components such as the fuel management module and attitude adjustment module. Model granularity and interface specifications: During the decomposition process, the granularity of each model and its interface properties are clearly defined to ensure consistent parameter transfer and interaction between components.

[0129] S22 model development and testing:

[0130] Component model development: Based on the system functional decomposition results, basic component models are developed using the Modelica language. For example, for the engine in the propulsion system, core components such as thrust calculation models and fuel consumption models are developed. Subsystem model development: Verified component models are integrated into subsystem models to ensure the accuracy of the logical and physical relationships between components within the subsystem. For example, the control subsystem model needs to integrate attitude control and timing control modules to achieve closed-loop control functions. Parameter calibration and model testing: Combining theoretical analysis with experimental data, key parameters of component and subsystem models are calibrated, and model accuracy is improved through iterative optimization. Test results cover both static and dynamic performance.

[0131] S23 system model development and integration:

[0132] System topology modeling: Based on system requirements and topology, component and subsystem models are integrated into a complete system model using drag-and-drop modeling tools. For example, a space vehicle model is constructed that includes multiple subsystems such as propulsion, thermal control, and control. System-level simulation and verification: The complete system model is verified in multiple scenarios using simulation platforms such as MWORKS. For example, performance such as thrust output and thermal management efficiency is verified in mission scenarios such as launch and recovery. Cross-domain collaborative verification: The coupling performance between subsystems is verified through multi-domain simulation analysis. For example, in the joint simulation of the propulsion and thermal control subsystems, fuel efficiency and temperature control stability are optimized.

[0133] S3 multi-dimensional immersive experience and evaluation optimization:

[0134] Based on the reusable space launch vehicle system model constructed in step 32, the various configurations of the space launch vehicle are rapidly optimized in combination with the model's three-dimensional virtual environment and configuration optimization algorithm to evaluate the feasibility and performance of the design scheme.

[0135] The specific steps are as follows:

[0136] S31 3D modeling and virtual environment creation:

[0137] 3D Model Construction: A 3D model of the space vehicle is constructed using a virtual simulation platform, encompassing key components such as the fairing, first stage, and second stage. Based on this 3D model, various launch and recovery scenarios, such as ignition, separation, and landing, are simulated, visually presenting the space vehicle's shape, structure, and motion.

[0138] Virtual environment simulation: In a virtual environment, designers observe the operating performance of space vehicles in different scenarios and optimize the design plan through virtual scene simulation.

[0139] S32 configuration optimization and evaluation:

[0140] Configuration Optimization Algorithm: Combining the model's inter-stage ratio optimization algorithm with the model-based program angle optimization algorithm, this algorithm rapidly optimizes various space vehicle configurations (such as the propulsion system configuration and control system parameter optimization), thereby improving spacecraft performance. For example, the rocket's dynamic characteristics and structural parameters are adjusted according to mission requirements. The design model implements dynamic closed-loop verification through an activity diagram model. After the model begins simulation, the design parameters are passed to the simulation model, which then runs and generates feedback, completing the design-simulation-verification closed-loop, enabling optimal performance to be achieved in the shortest possible time.

[0141] The inter-stage ratio optimization algorithm of the model specifically includes the following steps:

[0142] (1) Cost model establishment

[0143] The speed increment model is used to evaluate the speed increment required for the flight mission, thereby calculating the fuel consumption required to perform the mission, the speed increment ΔV mission The calculation formula is:

[0144] ΔV mission =ΔV orbit +ΔV g +ΔV d +ΔV p +ΔV r +ΔV margin

[0145] Where: ΔV orbit is the orbital velocity, ΔV r The relative velocity correction introduced by the Earth's rotation, ΔV g is the aerodynamic loss, ΔV d is the thrust loss, ΔV p is the gravity loss, ΔV margin is the safety margin;

[0146] (2) Parameter definition and modeling

[0147] The rocket system is divided into multiple stages, and the mass composition of each stage includes payload, fuel mass, structural mass, etc. Specifically, in the i-th rocket, define m p,i is the mass of propellant, which consists of fuel and oxidizer and is used to generate thrust; define m e,i is the structural mass; define m f,i The fuel that is not completely consumed after the rocket burns; define the mass ratio n i and the structural coefficient ∈ i The calculation formula is:

[0148]

[0149] (3) Optimization objectives and constraints

[0150] By CER=aM x is associated with the cost, where CER is the cost estimation relationship, x is the cost coefficient, M is the takeoff mass, a is the weighting coefficient, and the optimization objective is defined as minimizing the total takeoff mass GLOW of the rocket, which is calculated as follows:

[0151] min GLOW=m 0,1 +m e,b +m p,b

[0152] Among them, m 0,1 is the total mass of the second-stage rocket; m e,b is the mass of the first-stage rocket structure; m p,bis the mass of the first-stage rocket propellant;

[0153] Taking the Long March 5 as an example, for the two-stage reusable carrier rocket, the constraints are further defined: n i ≥1, 1.2≤TWR1≤2,0.5≤TWR i ≤3, i=2,…,N;

[0154] in, is the number of first-stage engines, TWR1 is the first thrust-to-weight ratio, TWR i is the i-th thrust-to-weight ratio.

[0155] (4) Problem solving and optimization

[0156] The optimization objectives and constraints are organized into a mixed integer nonlinear optimization problem. The velocity increment is calculated using the Tsiolkovsky equation to balance thrust loss and path deviation. The gravity loss ΔV during propulsion is calculated using integrals. g and path correction ΔV p,steering :

[0157]

[0158] Among them, t0 is the starting time, t f is the termination time, g is the acceleration due to gravity, γ is the flight path angle, T is the engine thrust, α is the angle of attack, and m is the rocket mass;

[0159] (5) Result output

[0160] The optimization results in the mass distribution, structural coefficient and thrust-to-weight ratio configuration among various stages, ultimately minimizing the total mass of the rocket system while meeting the ΔV requirements and performance indicators of the flight mission.

[0161] The program angle optimization algorithm of the model specifically includes the following steps:

[0162] (1) Optimization goal

[0163] The optimization objective is defined as minimizing the rocket fuel consumption z, which is calculated as:

[0164] z=min f

[0165] Where, f is fuel consumption;

[0166] (2) State variables and control variables

[0167] State variables Including horizontal position x, vertical position y, angle θ, horizontal speed Vertical speed Angular velocity and mass m;

[0168] Control variable u = [F, α], where F is thrust and α is angle of attack;

[0169] (3) System constraints

[0170] Let m be the mass of the rocket, I be the moment of inertia of the rocket, h be the height of the rocket, and κ be the fuel consumption coefficient per unit impulse. Establish the dynamic differential equation constraints of the rocket to describe the motion of the rocket in the world coordinate system, including the changes in position, angle, and velocity:

[0171]

[0172] (4) Initial and terminal constraints

[0173] Define the initial state and terminal state, where the initial state is the initial condition X0 of the launch point; the terminal state is the orbital element or landing condition X T ;

[0174] (5) Path and inequality constraints

[0175] Taking the Long March 5 as an example, for the two-stage reusable carrier rocket:

[0176] Thrust constraint F k :0 <F k <825500×9;

[0177] Angle constraint α k :

[0178] Dynamic load q and axial overload n x :q≤q max ,n x ≤n xmax ;

[0179] Where k is the interpolation number, k = 0, 1, ..., N, N is the number of collocation points, q max is the maximum dynamic pressure of the integrated vehicle, n xmax It is the maximum axial overload of the integrated carrier;

[0180] (6) Problem Discretization

[0181] The Legendre-Gauss-Lobatto pseudospectral method is used to discretize state variables and control variables into multiple time nodes. The state and control variables are fitted using the Lagrange interpolation function, and the differential equations are converted into algebraic equations to simplify the solution process.

[0182] Discrete form:

[0183]

[0184] Among them, X represents the state variable, X(T) represents the state variable at the node at time T, and X i represents the i-th element of the state vector X, L i (T) is the basis function of the Lagrange interpolation function;

[0185] The dynamic equations are solved at discrete nodes:

[0186]

[0187] Among them, T K represents the time K, Indicates time T K The derivative of the state variable, Represents the derivative of the state variable at the discrete node, the differential matrix D Ki Represents the differential value of each basis function of the Lagrange interpolation function at each LGL point;

[0188] (7) Discretization of constraints

[0189] Convert the initial conditions, terminal conditions, path constraints, and inequality constraints into algebraic constraint relations at discrete points; the initial and terminal conditions are:

[0190]

[0191] Where x is used to specify the horizontal displacement of the landing point from the initial point, which can be determined in combination with the location of the recovery station. The inequality constraint is discretized as follows: the thrust limit is discretized F k for:

[0192] 0 <F k <825500×9,k=0,1,...,N

[0193] Angle-limited discrete α k for:

[0194]

[0195] Where N is the number of points.

[0196] (8) Problem Solving

[0197] The optimization problem is handed over to the solver for linearization and solution to obtain the optimal fuel consumption, thrust and program angle.

[0198] Through the above technical solutions, the present invention effectively addresses existing issues such as the separation of design and verification, insufficient interdisciplinary collaboration, and slow optimization processes. It promotes the digitalization and automation of complex equipment system design and verification, and improves the overall efficiency and accuracy of equipment research and development. Applied to the design and verification of complex engineering systems, the present invention provides important technical support for the efficient development of complex equipment systems.

[0199] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A model-based integrated design and verification method for complex equipment systems, characterized by: The following steps are involved: S1 multi-scenario demand analysis and solution design: Based on the four-level framework of "demand-behavior-structure-parameters" and combined with mission scenario requirements, we conduct specialized design and analysis to build a full-process design and analysis method for reusable space vehicle systems. S2 multi-domain system modeling and comprehensive verification: Based on the full-process design and analysis method of step S1, starting from demand analysis, the complex system is decomposed and the modeling granularity is clarified; Based on the system decomposition results, component models, subsystem models, and system models are developed from the bottom up. Parameter calibration and performance verification of the models are carried out by combining theoretical and experimental data. A reusable space vehicle system model is constructed, and comprehensive system analysis and verification is carried out. S3 multi-dimensional immersive experience and evaluation optimization: Based on the reusable space vehicle system model constructed in step S2, the various configurations of the space vehicle are rapidly optimized by combining the model's three-dimensional virtual environment and the configuration optimization algorithm. The specific steps are as follows: S31 3D modeling and virtual environment creation: By building a three-dimensional model of the space vehicle through a virtual simulation platform and simulating different launch and recovery scenarios, designers can observe the space vehicle's operational performance in different scenarios and optimize the design through virtual scenario simulation. S32 configuration optimization and evaluation: By combining the model's inter-stage ratio optimization algorithm and the model-based program angle optimization algorithm, various configurations of space launch vehicles can be quickly optimized. The inter-stage ratio optimization algorithm of the model specifically includes the following steps: (1) Cost model establishment The speed increment model is used to evaluate the speed increment required for the flight mission, thereby calculating the fuel consumption required to perform the mission, the speed increment ΔV mission The calculation formula is: ΔV mission =ΔV orbit +ΔV g +ΔV d +ΔV p +ΔV r +ΔV margin Where: ΔV orbit is the orbital velocity, ΔV r The relative velocity correction introduced by the Earth's rotation, ΔV g is the aerodynamic loss, ΔV d is the thrust loss, ΔV p is the gravity loss, ΔV margin is the safety margin; (2) Parameter definition and modeling The rocket system is divided into multiple stages. In the i-th rocket, define m p,i is the mass of propellant, which consists of fuel and oxidizer and is used to generate thrust; define m e,i is the structural mass; define m f,i The fuel that is not completely consumed after the rocket burns; define the mass ratio n i and the structural coefficient ∈ i The calculation formula is: (3) Optimization objectives and constraints By CER=aM x is associated with the cost, where CER is the cost estimation relationship, x is the cost coefficient, M is the takeoff mass, a is the weighting coefficient, and the optimization objective is defined as minimizing the total takeoff mass GLOW of the rocket, which is calculated as follows: min GLOW=m 0,1 +m e,b +m p,b Among them, m 0,1 is the total mass of the second-stage rocket; m e,b is the mass of the first-stage rocket structure; m p,b is the mass of the first-stage rocket propellant; (4) Problem solving and optimization The optimization objectives and constraints are organized into a mixed integer nonlinear optimization problem. The velocity increment is calculated by the Tsiolkovsky equation to balance the thrust loss and path deviation. The gravity loss ΔV during propulsion is calculated using integrals. g and path correction ΔV p,steering : Among them, t0 is the starting time, t f is the termination time, g is the acceleration due to gravity, γ is the flight path angle, T is the engine thrust, α is the angle of attack, and m is the rocket mass; (5) Result output The optimization results in the mass distribution, structural coefficient and thrust-to-weight ratio configuration among various stages, ultimately minimizing the total mass of the rocket system while meeting the ΔV requirements and performance indicators of the flight mission.

2. The model-based integrated design and verification method for complex equipment systems according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11 determines mission requirements and design goals: Based on the task scenario, key requirements are extracted and gradually decomposed to the system and subsystem levels to form a systematic design goal. SysML use case diagrams are used to clarify the main functions in the task scenario and the interaction between the system and external objects. The global requirements are then hierarchically decomposed through SysML requirement diagrams to extract the key functions and performance requirements of the system and define the division of tasks for each subsystem. S12 system function analysis and dynamic behavior modeling: Through dynamic behavior modeling, the key system functions extracted in step S11 are decomposed into multiple specific task activities. SysML activity diagrams are used to describe the dynamic behavior and interaction processes between functional modules, clarifying the execution order and logical relationships of tasks. For each subsystem, a state machine diagram is established to analyze its working mode and state transitions in different task scenarios, and define the trigger conditions and transition paths between states. Through the combination of activity diagrams and state machine diagrams, the dynamic response and functional implementation methods of the system in the time dimension are comprehensively analyzed, and the collaborative work of various functional modules is verified to ensure that the system can operate efficiently under different task requirements. S13 system architecture design and model construction: Starting from the global functions, the modular composition of the rocket system is clarified. The functional division of labor and interface relationships between modules are defined through SysML BDD diagrams. SysML IBD diagrams are used to describe in detail the connection relationships between the components within the subsystem. Through a unified data interface and simulation platform, the product topology is combined with the professional profile architecture to ensure consistency from top-level design to detailed implementation. S14 parameter definition and system verification: Through the definition and simulation verification of design parameters, the system performance is ensured to meet the mission requirements.

3. The model-based integrated design and verification method for complex equipment systems according to claim 2, characterized in that: The verification method in step S14 includes static indicator verification and dynamic performance verification. The static indicator verification is to verify the static performance of the system using SysML parameter diagrams; the dynamic performance verification is to verify the performance of the rocket in different mission scenarios in combination with dynamic simulation models.

4. The model-based integrated design and verification method for complex equipment systems according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21 system breakdown: Based on mission requirements, the system functions are decomposed layer by layer into several subsystems, components, and assemblies until they can be directly described through physical models and modeling languages. The reusable space vehicle is decomposed into several subsystems, and then further decomposed into fuel management modules and attitude adjustment modules. During the decomposition process, the granularity of each model and its interface properties are clarified to ensure the consistency of parameter transfer and interaction between components. S22 model development and testing: Based on the system decomposition results of step S21, a basic component model is developed using the Modelica language. The verified component models are integrated into a subsystem model to ensure the accuracy of the logical and physical relationships between the components within the subsystem. Combining theoretical analysis with experimental data, the key parameters of the component and subsystem models are calibrated. Model accuracy is improved through iterative optimization, and the test results cover both static and dynamic performance. S23 system model development and integration: Based on system requirements and topology, component and subsystem models are integrated into a complete system model using drag-and-drop modeling tools. The complete system model is verified in multiple scenarios through the MWORKS simulation platform, and the coupling performance between subsystems is verified through multi-domain simulation analysis.

5. The model-based integrated design and verification method for complex equipment systems according to claim 1, characterized in that: For the two-stage reusable launch vehicle, the constraints are further defined: n i ≥1, 1.2≤TWR1≤2,0.5≤TWR i ≤3, i=2,…,N; in, is the number of first-stage engines, TWR1 is the first thrust-to-weight ratio, TWR i is the i-th thrust-to-weight ratio.

6. The model-based integrated design and verification method for complex equipment systems according to claim 1, characterized in that: The program angle optimization algorithm of the model specifically includes the following steps: (1) Optimization goal The optimization objective is defined as minimizing the rocket fuel consumption z, which is calculated as: z=min f Where, f is fuel consumption; (2) State variables and control variables State variables Including horizontal position x, vertical position y, angle θ, horizontal speed Vertical speed Angular velocity and mass m; Control variable u = [F, α], where F is the thrust; (3) System constraints Let I be the moment of inertia of the rocket, h be the height of the rocket, and κ be the fuel consumption coefficient per unit impulse. Establish the dynamic differential equation constraint of the rocket to describe the motion of the rocket in the world coordinate system, including the changes in position, angle, and velocity. The formula is: (4) Initial and terminal constraints Define the initial state and terminal state, where the initial state is the initial condition X0 of the launch point; the terminal state is the orbital element or landing condition X T ; (5) Path and inequality constraints For a two-stage reusable launch vehicle: thrust constraint F k :0 <F k <825500×9; Angle constraint α k : Dynamic load q and axial overload n x :q≤q max ,n x ≤n xmax ; Among them, k is the interpolation number, q max is the maximum dynamic pressure of the integrated vehicle, n xmax It is the maximum axial overload of the integrated carrier; (6) Problem Discretization The Legendre-Gauss-Lobatto pseudospectral method is used to discretize state variables and control variables into multiple time nodes. The state and control variables are fitted using the Lagrange interpolation function, and the differential equations are converted into algebraic equations to simplify the solution process. Discrete form: Among them, X represents the state variable, X(T) represents the state variable at the node at time T, and X i represents the i-th element of the state vector X, L i (T) is the basis function of the Lagrange interpolation function; The dynamic equations are solved at discrete nodes: Among them, T K represents the time K, Indicates time T K The derivative of the state variable, Represents the derivative of the state variable at the discrete node, the differential matrix D Ki Represents the differential value of each basis function of the Lagrange interpolation function at each LGL point; (7) Discretization of constraints Convert initial conditions, terminal conditions, path constraints and inequality constraints into algebraic constraint relations at discrete points; (8) Problem Solving The optimization problem is handed over to the solver for linearization and solution to obtain the optimal fuel consumption, thrust and program angle.

7. The model-based integrated design and verification method for complex equipment systems according to claim 6, characterized in that: Initial condition X0 and terminal condition X T for: Among them, x is used to specify the horizontal displacement of the landing point from the initial point, which is determined in combination with the location of the recycling station; The inequality constraint is discretized as follows: the thrust constraint is discretized F k for: 0<F k <825500×9,k=0,1,...,N Angle-limited discrete α k for: Where N is the number of points.

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