Aircraft co-simulation integrated development system

Abstract

The invention discloses an aircraft co-simulation integrated development system and an operation method. The aircraft co-simulation integrated development system comprises a concept scene development module, a demand modeling management module, a system structure modeling module, a multidisciplinary optimization module and a simulation deduction verification module. The concept scene development module inputs a flight task and executes a scene scenario scheme of confrontation environment construction simulation associated with the flight task; the demand modeling management module decomposes a scene scenario scheme into modeling requirements of multiple single subjects, and inputs the modeling requirements into the system structure modeling module according to the subjects; each piece of single-subject modeling software of the system structure modeling module receives a corresponding modeling requirement and executes single-subject design modeling; the multidisciplinary optimization module is used for integrating the single-disciplinary modeling models of the disciplines to generate a unified multidisciplinary model; and the simulation deduction verification module receives the input flight task, the multidisciplinary model of the aircraft and the scene scenario scheme to execute task simulation exercise.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft simulation, and in particular to an aircraft collaborative simulation integrated development system. Background Art

[0002] The application of multidisciplinary design optimization technology abroad is relatively mature. The American Aerospace Corporation has developed an integrated design software system FONSIZE. Lockheed Martin has developed a rapid aircraft concept design system, and its "Delta" series launch vehicle design also applies multidisciplinary design optimization technology. The Jet Propulsion Laboratory of the United States has designed the Mars rover using MDO technology. Boeing has developed an aircraft MDO system using a high-simulation analysis model, and successfully tested the X-51A hypersonic cruise missile, conquering the hypersonic integration technology. The US F-22 fighter uses multidisciplinary design optimization technology to comprehensively optimize its aerodynamic layout, structural layout, strength, stiffness and control system, solving the problem that the flutter speed of the vertical tail does not meet the design specification requirements in the early stage of model design. The Multidisciplinary Design Optimization Extension (EXPE-DITE) project, a collaboration between the US Air Force Research Laboratory (AFRL) and Lockheed Martin, aims to promote the development of multidisciplinary analysis and design optimization technology in the United States.

[0003] The U.S. Navy has established the Hypersonic Vehicle Integration Tool (IHAT), which integrates commercial analysis software such as Patran, GASP, OverFlow, POST, SINDA, and ModelCenter. The entire multidisciplinary module structure is packaged in the DAKOTA optimization cycle, which can achieve system-level optimization. IHAT is interconnected with the "Virtual Missile Integration (VMI)" tool and the "Battle Analysis Integration (WAI)" tool to better understand its potential effectiveness in a combat environment during the conceptual design phase. In addition to the United States, European countries have carried out a lot of work in this regard, such as the EU's AGILE project and its follow-up project AGILE4.0.

[0004] Currently, the most commonly used general MDO (Multidisciplinary Design Optimization) software platforms include iSight from Enginous, MODELCENTER from Phoenix Integration, and OPTIMUS from NoesisSolution.

[0005] Attached Figure 1 Is the existing Isight optimization process flow chart; Attachment Figure 2 It is an overview of Isight data flow.

[0006] MDO system refers to multidisciplinary design optimization system, which is a tool that integrates multiple disciplines and uses computer technology to design complex systems. MDO system integrates models, methods and algorithms of multiple disciplines into a unified environment to achieve the goal of multidisciplinary collaborative design and optimization.

[0007] Domestic researchers have applied the general MDO software platform to research in many fields. Wang Junfeng et al. conducted multidisciplinary optimization of the launch vehicle based on MODEL CENTER, greatly shortening the design cycle while ensuring accuracy. Zhou Jian et al. used the OPTIMUS platform to optimize the parameters of the trajectory control system and achieved the optimization design goal of reducing the pitch angle at the time of landing. Zhang Rui et al. used the OPTIMUS platform to study the optimization problem of the reentry trajectory design of near-space aircraft, improving the efficiency of optimization design.

[0008] There is currently a lack of multidisciplinary optimization design simulation platforms in China.

[0009] On the one hand, it is difficult to achieve accurate characterization and modeling because advanced aircraft involve many disciplines, a wide range, and high complexity. On the other hand, some current joint optimization design software platforms either only focus on the realization of the performance indicators of the aircraft itself and ignore applicability; or only focus on the special assessment of performance indicators and ignore the fine-grained support for the performance design of the aircraft itself.

[0010] There is a lack of mechanism models suitable for multi-discipline coupling research on advanced aircraft in China.

[0011] The overall design of advanced aircraft involves multiple disciplines that are closely coupled, and the uncertainty impacts between disciplines are also cross-transmitted. On the one hand, if only the independent optimization of a single discipline such as aerodynamics, mechanics, structure, strength, control, and power is considered, it is very likely that the overall performance will be far below the expected requirements; on the other hand, an overly complex mechanism model will greatly increase the computational burden, and even fail to obtain the optimization results. An overly simple multi-disciplinary mechanism model has large errors, resulting in large deviations in the optimization results, thus losing the meaning of joint optimization. Therefore, to construct an applicable mechanism model, it is necessary not only to adopt a holistic approach to study the overall uncertainty design optimization problem involving multiple professional couplings and multi-physical fields, but also to pay attention to the rationality and effectiveness of the mechanism models of multiple disciplines such as mechanics, aerodynamics, control, quality, structure, strength, and power that have a greater impact on the aircraft.

[0012] In recent years, the research on multidisciplinary design optimization has flourished in China, and there has been great progress in the theory and application of multidisciplinary design optimization. The gap with foreign countries is narrowing, but there is still huge room for development and potential. In particular, many researchers currently focus on the fields of hypersonic aircraft, space shuttles, manned spacecraft, cruise aircraft, drones, mechanical design, etc., but for advanced aircraft with wide applications in the aerospace field, there are few studies focusing on multidisciplinary integrated parameter optimization methods for advanced aircraft. Summary of the invention

[0013] A first aspect of the present invention provides an aircraft collaborative simulation integrated development system, the aircraft collaborative simulation integrated development system comprising: a concept scenario development module, a demand modeling management module, a system structure modeling module, a multidisciplinary optimization module and a simulation deduction verification module;

[0014] The conceptual scenario development module constructs a simulated scenario scenario based on a flight mission input by a specified aircraft model and a confrontation environment associated with executing the flight mission;

[0015] The demand modeling management module decomposes the scenario scenario into multiple single-discipline modeling requirements, and inputs the modeling requirements into the system structure modeling module according to the disciplines;

[0016] The system structure modeling module includes a plurality of single-discipline modeling software, each of which receives a corresponding modeling requirement and performs single-discipline design modeling;

[0017] The multidisciplinary optimization module integrates the single-disciplinary modeling models of each discipline to generate a unified multidisciplinary model;

[0018] The simulation and deduction verification module receives input of the aircraft flight mission, the aircraft's multidisciplinary model and scenario scenario to perform mission simulation exercises to verify the aircraft's mission execution capability.

[0019] In the system of the first aspect of the present invention, the scenario scenario constructed by the concept scenario development module includes: aircraft missions and mission completion indicators;

[0020] The mission completion indicators include: penetration success rate, ammunition hit rate, and ammunition damage probability.

[0021] As in the system described in the first aspect of the present invention, the single-discipline modeling software of the system structure modeling module receives discipline modeling requirements and executes single-discipline design modeling; establishes model input drive and performs model verification on the modeled model to obtain single-discipline model simulation results.

[0022] As in the system described in the first aspect of the present invention, the multidisciplinary optimization module receives the model data and model simulation data established by each single discipline, performs data conversion, model integration, operation coordination and model optimization on the data output of each discipline software, and generates a final multidisciplinary model.

[0023] As for the system described in the first aspect of the present invention, the model integration includes: establishing the relationship between variables between different subject models, clarifying the input and output of variables between each subject, and configuring parameters of the optimizer applied during model optimization to determine the optimization target.

[0024] In the system as described in the first aspect of the present invention, determining the optimization goal includes: determining the objective function based on the final optimization requirements; designing variables that are directly optimized or indirectly restricted in the optimization process; and determining the type of constraints and the numerical range of the constraints.

[0025] A second aspect of the present invention provides an aircraft collaborative simulation method, the method running on any of the above-mentioned systems, the method comprising the following steps:

[0026] Step 1, inputting a flight mission for a specified aircraft model and constructing a simulated scenario scenario for a confrontation environment associated with executing the flight mission;

[0027] Step 2: Decompose the scenario scenario into modeling requirements of multiple disciplines, and input the modeling requirements into corresponding single-discipline modeling software according to the disciplines;

[0028] Step 3, each single-discipline modeling software receives the corresponding modeling requirements, designs a single-discipline model and performs model verification on the model;

[0029] Step 4: Integrate each single-discipline modeling model to generate a unified multidisciplinary model;

[0030] Step 5: Perform mission simulation exercises using multidisciplinary models and scenario scenarios to verify the mission capabilities of the aircraft.

[0031] As described in the second aspect of the present invention, step 4 includes: if the data of the received single-discipline model is incompatible with the data of other single-discipline models, returning the incompatible single-discipline model to step 3 and performing single-discipline model modification.

[0032] As described in the second aspect of the present invention, the task simulation exercise process is pushed to a display device for display.

[0033] The method of the present invention has the following advantages:

[0034] A collaborative optimization algorithm is established, and a variety of algorithms are used to coordinate the complex relationships of multiple disciplines to achieve the purpose of optimized design. Multidisciplinary modeling is adopted, and each discipline establishes a corresponding model, and these models must be able to relate to and influence each other. Data sharing and integration: Data and information are shared between different disciplines, and the system has the ability to integrate different data sources to ensure the accuracy and real-time nature of the data. The system of the present invention is scalable and can adapt to aircraft design projects of different sizes and complexities. At the same time, the use of modular design helps to improve the flexibility and maintainability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the existing Isight optimization process flow chart;

[0036] Figure 2 It is an overview of Isight data flow;

[0037] Figure 3 It is the complex system collaborative simulation process of the present invention;

[0038] Figure 4 Aircraft collaborative simulation integrated development prototype system. DETAILED DESCRIPTION

[0039] The research and development and design of advanced aircraft technology is much more difficult than traditional aircraft technology because it needs to solve the complex near-space atmospheric environment, its own complex aerodynamic / aerothermal characteristics, large-span flight envelope, and high lift-to-drag ratio, high loading ratio aerodynamic layout design requirements and other issues, which bring many difficulties in the theory and engineering implementation of the overall design of advanced aircraft.

[0040] First, the diversified capability requirements have greatly increased the design difficulty of advanced aircraft. With the continuous improvement of systematized capability requirements, when using weapons and equipment to implement long-range precision strikes, in order to break through the air defense and anti-missile system of a powerful enemy, it is often hoped that advanced aircraft will have the performance of high-altitude, high-speed penetration and precision strike. At the same time, in order to expand battlefield applicability, its multi-purpose and multi-task capabilities are also emphasized. In order to improve the penetration capability, when passing through the threat area, it is necessary to enhance its hypersonic or stealth penetration capability. The different flight missions make it difficult for aircraft with fixed aerodynamic shapes to meet such complex and changeable flight mission requirements, and greatly increase the design difficulty of the overall plan of advanced aircraft.

[0041] Second, the multi-disciplinary coupling contradiction constraint is a key problem that cannot be avoided in the design of advanced aircraft. For example, high lift-to-drag ratio and high loading ratio are the two main design goals pursued by the aerodynamic layout design of advanced aircraft. High loading ratio is the basis and prerequisite for the aircraft to complete the flight mission. High lift-to-drag ratio has a very important impact on the maximum range, lateral flight and other performance of the aircraft. High lift-to-drag ratio can not only reduce the maximum overload of the nose, but also reduce the maximum heat flow of the nose during gliding. More importantly, high lift-to-drag ratio can increase the lateral flight capability of the nose and improve the survivability and penetration capability of the nose. Existing boost-glide aircraft are usually designed as wing-body fusion layout, lifting body layout or waverider layout. These layout forms are difficult to handle for a long time. Heat protection is difficult to miniaturize, the volume utilization rate is low, and it is difficult to integrate with the rocket carrier. There is a serious conflict between the lift-to-drag ratio and the volume ratio, and even an approximately inverse relationship. This also brings great obstacles to the overall design. In addition, performance requirements such as wide envelope and large flight have greatly increased the complexity of advanced aircraft design, posing greater challenges to the speed, accuracy and safety of their control systems.

[0042] Third, the redundancy and conservatism caused by the decoupled independent optimization design between traditional aerodynamics, mechanics, control, heat insulation, power and other disciplines limit the maximization of overall performance. The overall design of advanced aircraft involves multiple disciplines and the disciplines are closely coupled, and the uncertainty impacts between disciplines are also cross-transmitted. If only the independent optimization of a single discipline such as aerodynamics, mechanics, structure, strength, control, and power is considered, it is very likely that the overall performance will be far below the expected requirements. This is even more important for advanced aircraft at the cross-cutting frontier technology in the aerospace field. Therefore, it is necessary to adopt a holistic approach to study the overall uncertainty design optimization problem of aircraft involving multiple professional coupling and multi-physical field coupling. In particular, it is necessary to focus on multiple disciplines that have a greater impact on aircraft, such as mechanics, aerodynamics, control, quality, structure, strength, power, etc., and improve the overall aircraft design performance by tapping the synergy between coupled disciplines.

[0043] Fourth, there is a lack of a multidisciplinary integrated optimization design simulation and testing platform for performance transfer, especially in the field of overall aircraft design, where the demand is more urgent. For a long time, independent optimization design of multiple disciplines such as aerodynamics, mechanics, control, heat insulation, and power has been difficult to achieve accurate representation and modeling due to the large number of fields involved and the high technical complexity. At the same time, overly complex representation models will greatly increase the computational burden of joint optimization, and even cause "dimensionality disaster" and fail to obtain the optimal solution; on the other hand, some current joint optimization design software platforms either only focus on the realization of the performance indicators of the aircraft itself and ignore applicability, or only focus on the special assessment of performance indicators and ignore the fine-grained support of the performance of the aircraft itself.

[0044] The curse of dimensionality refers to the phenomenon that in computational problems involving vectors, the amount of computation increases exponentially as the number of dimensions increases. This phenomenon is particularly evident in high-dimensional spaces, making available data sparse and the performance of machine learning models degraded.

[0045] The curse of dimensionality is reflected in many fields, such as digital analysis, sampling, combination, machine learning, data mining and database, etc. In machine learning, the curse of dimensionality may cause the training time and space complexity of the model to increase sharply as the dimension of the input features increases, and may even cause the algorithm to fail to converge within an acceptable time.

[0046] The technical scheme of the present invention will be clearly and completely described below in conjunction with specific embodiments, but those skilled in the art should understand that the embodiments described below are only used to illustrate the present invention and should not be regarded as limiting the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Attached Figure 4 It is an aircraft collaborative simulation integrated development prototype system

[0048] A first aspect of the present invention provides an aircraft collaborative simulation integrated development system, the aircraft collaborative simulation integrated development system comprising: a concept scenario development module, a demand modeling management module, a system structure modeling module, a multidisciplinary optimization module and a simulation deduction verification module;

[0049] The conceptual scenario development module constructs a simulated scenario scenario based on a flight mission input by a specified aircraft model and a confrontation environment associated with executing the flight mission;

[0050] The demand modeling management module decomposes the scenario scenario into multiple single-discipline modeling requirements, and inputs the modeling requirements into the system structure modeling module according to the disciplines;

[0051] The system structure modeling module includes a plurality of single-discipline modeling software, each of which receives a corresponding modeling requirement and performs single-discipline design modeling;

[0052] The multidisciplinary optimization module integrates the single-disciplinary modeling models of each discipline to generate a unified multidisciplinary model;

[0053] The simulation and deduction verification module receives input of the aircraft flight mission, the aircraft's multidisciplinary model and scenario scenario to perform mission simulation exercises to verify the aircraft's mission execution capability.

[0054] In the system of the first aspect of the present invention, the scenario scenario constructed by the concept scenario development module includes: aircraft missions and mission completion indicators;

[0055] The mission completion indicators include: penetration success rate, ammunition hit rate, and ammunition damage probability.

[0056] As in the system described in the first aspect of the present invention, the single-discipline modeling software of the system structure modeling module receives discipline modeling requirements and executes single-discipline design modeling; establishes model input drive and performs model verification on the modeled model to obtain single-discipline model simulation results.

[0057] As in the system described in the first aspect of the present invention, the multidisciplinary optimization module receives the model data and model simulation data established by each single discipline, performs data conversion, model integration, operation coordination and model optimization on the data output of each discipline software, and generates a final multidisciplinary model.

[0058] As for the system described in the first aspect of the present invention, the model integration includes: establishing the relationship between variables between different subject models, clarifying the input and output of variables between each subject, and configuring parameters of the optimizer applied during model optimization to determine the optimization target.

[0059] In the system as described in the first aspect of the present invention, determining the optimization goal includes: determining the objective function based on the final optimization requirements; designing variables that are directly optimized or indirectly restricted in the optimization process; and determining the type of constraints and the numerical range of the constraints.

[0060] Example 1

[0061] The comprehensive optimization design of advanced aircraft under multi-disciplinary and multi-objective constraints is a combination of optimization problems in various disciplines, rather than a simple superposition. It should include all possible constraints and design variables in all disciplines, and fully consider potential disciplinary conflicts to avoid "over-constraints" and "non-independent design variables". In the optimization design process, the following optimization factors need to be considered:

[0062] ①Objective function: The final requirement of optimization is to minimize the takeoff mass as the objective function. The optimal fuel consumption is obtained through mechanical calculation and fed back to the mass module. The structural mass is re-estimated to obtain the minimum takeoff mass that meets the constraints.

[0063] ② Design variables: The variables that can be optimized in the overall plan of advanced aircraft include geometry, aerodynamics, mechanics, heat insulation, quality and other disciplines. They can be specifically divided into design variables as shown in the following table. These design variables are variables that need to be directly optimized or indirectly restrict the optimization process. The optimization process of design variables follows the guidance of the optimization algorithm, and the number of design variables also directly affects the speed of optimization iteration convergence.

[0064] The relationship between advanced aircraft design variables and their disciplines is shown in the following table:

[0065] Table 1

[0066] Serial number Design variables Discipline 1 Overall length of advanced aircraft geometry 2 Advanced aircraft tail width geometry 3 Advanced aircraft wing ratio geometry 4 Maximum range Mechanics 5 Descent speed Mechanics 6 Heat flux Heat insulation 7 Maximum load mass structure

[0067] ③Constraints

[0068] Geometry: Payload limitations of advanced aircraft; Equipment bay limitations; External mechanical structure limitations under design use conditions.

[0069] Aerodynamics: maximum outline size restrictions; sufficient internal volume; lift-drag characteristic constraints.

[0070] Mechanics: load separation speed; load separation height; dynamic pressure, overload, heat flow, etc.

[0071] Thermal protection discipline: cabin temperature; thermal protection structure mass constraints.

[0072] Table 2 illustrates the types of constraints and the value ranges of the constraints.

[0073] Table 2

[0074] Serial number type Constraint (Example) 1 Advanced aircraft gross mass ≤2.5t 2 Maximum length of advanced aircraft 4m 3 Maximum diameter of advanced aircraft 2.1m 4 Load separation speed 7200m / s 5 Load separation height 70km 6 Dynamic Pressure ≤12000Pa 7 Normal Overload ≤4.5g 8 Cold structure temperature ≤350K

[0075] (2) Optimization process of advanced aircraft under multi-objective and multi-constraint conditions

[0076] ①Task target requirements

[0077] Before the optimization process of the advanced aircraft, the flight conditions of the advanced aircraft need to be limited. According to the input requirements, the requirements of the advanced aircraft are shown in the following table:

[0078] The required parameters of advanced aircraft are shown in Table 3

[0079] Table 3

[0080] serial number Requirement Name Data Value 1 Minimum flight distance (km) 500 2 Maximum flight range (km) 2000 3 Maximum lateral flight range (km) 4000 4 Maximum descent flight range (km) 200 5 Maximum normal overload (G) 35 6 Flight speed (Mach number) 23 7 Falling speed (Mach number) 2.5 8 Load mass (kg) 750 9 RCS (probability of detection) (m^2) 8 10 Situational awareness detection distance (km) 20

[0081] The required parameters of advanced aircraft are mainly divided into two categories. One is the parameters that need to be optimized and the parameters that serve as the target. The other is the restriction parameters, which mainly play a role in limiting the output results during the optimization. Subsequent optimization is carried out according to the requirement document, and the main optimization goal is the minimum mass of the advanced aircraft.

[0082] ②Platform Integration

[0083] According to the input conditions and the adjusted optimization algorithm, the optimization process based on Isight is carried out, and the overall optimization process is shown as follows. In Isight, the relationship between multiple disciplines is established, the connection between variables between different discipline models is established, the input and output of variables between disciplines are clarified, and the parameters of the applied optimizer are configured more reasonably, and the optimization target is clarified, so as to obtain better optimization results. At the same time, in the process of advanced aircraft design, there is coupling, data exchange, and data communication between variables in different disciplines.

[0084] A second aspect of the present invention provides an aircraft collaborative simulation method, the method running on any of the above-mentioned systems, the method comprising the following steps:

[0085] Step 1, constructing a simulated scenario plan for a designated aircraft input flight mission and a confrontation environment associated with executing the flight mission;

[0086] Step 2: Decompose the scenario scenario into modeling requirements of multiple disciplines, and input the modeling requirements into corresponding single-discipline modeling software according to the disciplines;

[0087] Step 3, each single-discipline modeling software receives the corresponding modeling requirements, designs a single-discipline model and performs model verification on the model;

[0088] Step 4: Integrate each single-discipline modeling model to generate a unified multidisciplinary model;

[0089] Step 5: Perform mission simulation exercises using multidisciplinary models and scenario scenarios to verify the mission capabilities of the aircraft.

[0090] As described in the second aspect of the present invention, step 4 includes: if the data of the received single-discipline model is incompatible with the data of other single-discipline models, returning the incompatible single-discipline model to step 3 and performing single-discipline model modification.

[0091] As described in the second aspect of the present invention, the task simulation exercise process is pushed to a display device for display.

[0092] Attached Figure 3 This is the complex system collaborative simulation process of the present invention.

[0093] Example 2

[0094] The multidisciplinary modeling method based on integrated optimization software uses integrated optimization software and interfaces of various disciplines to jointly establish a multidisciplinary field model. This method uses integrated optimization software to solve data interaction, multidisciplinary optimization algorithms and collaborative simulation between various disciplines, and has good openness, multidisciplinary optimization and distributed computing capabilities. This method can simply and conveniently implement multidisciplinary modeling, and does not limit the number and types of multidisciplinary modeling software, and can give priority to the simulation needs of various disciplines.

[0095] According to the needs of collaborative simulation of complex system models, ISIGHT software is used in combination with software from various professional fields to jointly establish multidisciplinary simulation and optimization models to complete the collaborative simulation and modeling process of multidisciplinary models.

[0096] The multidisciplinary modeling process can be divided into two stages: single-discipline modeling and multidisciplinary integration. In the single-discipline modeling stage, the system multidisciplinary modeling requirements are decomposed into the modeling requirements of each discipline, and the design work involved in the software of each discipline is completed for modeling, and these models are verified to ensure the validity of the models.

[0097] In the multidisciplinary modeling stage, we first study the input drive and simulation result output of the models established by each discipline. If necessary, we need to modify each single discipline model to facilitate data conversion, transmission, ISIGHT integration and operation coordination between discipline software. In this stage, we mainly complete the integration, operation coordination and optimization of each discipline model to form a final usable multidisciplinary model.

[0098] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. An aircraft collaborative simulation integrated development system, characterized in that: The aircraft collaborative simulation integrated development system includes: a concept scenario development module, a demand modeling management module, a system structure modeling module, a multidisciplinary optimization module and a simulation deduction verification module; The conceptual scenario development module constructs a simulated scenario scenario based on a flight mission input by a specified aircraft model and a confrontation environment associated with executing the flight mission; The demand modeling management module decomposes the scenario scenario into multiple single-discipline modeling requirements, and inputs the modeling requirements into the system structure modeling module according to the disciplines; The system structure modeling module includes a plurality of single-discipline modeling software, each of which receives a corresponding modeling requirement and performs single-discipline design modeling; The multidisciplinary optimization module integrates the single-disciplinary modeling models of each discipline to generate a unified multidisciplinary model; The simulation and deduction verification module receives input of the aircraft flight mission, the aircraft's multidisciplinary model and scenario scenario to perform mission simulation exercises to verify the aircraft's mission execution capability.

2. The system according to claim 1, characterized in that The scenario scenario constructed by the conceptual scenario development module includes: aircraft missions and mission completion indicators; The mission completion indicators include: penetration success rate, ammunition hit rate, and ammunition damage probability.

3. The system according to claim 1, characterized in that The single-discipline modeling software of the system structure modeling module receives discipline modeling requirements and executes single-discipline design modeling; establishes a model input drive and performs model verification on the modeled model to obtain a single-discipline model simulation result.

4. The system according to claim 1, characterized in that The multidisciplinary optimization module receives the model data and model simulation data established by each single discipline, performs data conversion, model integration, operation coordination and model optimization on the data output of each discipline software, and generates a final multidisciplinary model.

5. The system according to claim 4, characterized in that The model integration includes: establishing the relationship between variables of different subject models, clarifying the input and output of variables between subjects, and configuring parameters of the optimizer used in model optimization to determine the optimization target.

6. The system according to claim 5, characterized in that The determination of the optimization goal includes: determining the objective function according to the final optimization requirement; designing the variables to be directly optimized or indirectly restricted in the optimization process; and determining the type of constraints and the numerical range of the constraints.

7. An aircraft collaborative simulation method, the method being run on the system according to any one of claims 1 to 6, characterized in that: The method comprises the following steps: Step 1, inputting a flight mission for a specified aircraft model and constructing a simulated scenario scenario for a confrontation environment associated with executing the flight mission; Step 2: Decompose the scenario scenario into modeling requirements of multiple disciplines, and input the modeling requirements into corresponding single-discipline modeling software according to the disciplines; Step 3, each single-discipline modeling software receives the corresponding modeling requirements, designs a single-discipline model and performs model verification on the model; Step 4: Integrate each single-discipline modeling model to generate a unified multidisciplinary model; Step 5: Perform mission simulation exercises using multidisciplinary models and scenario scenarios to verify the mission capabilities of the aircraft.

8. The method according to claim 7, characterized in that Step 4 includes: if the data of the received single-discipline model is incompatible with the data of other single-discipline models, returning the incompatible single-discipline model to step 3, and performing single-discipline model modification.

9. The method according to claim 7, characterized in that The task simulation exercise process is pushed to a display device for display.

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