Aircraft overall optimization design method and device and electronic equipment

Abstract

The invention provides an aircraft overall optimization design method and device and electronic equipment, and relates to the technical field of aircraft overall design. The method comprises the following steps: calculating the weight of each part of an aircraft based on aircraft design parameters, and calculating the maximum takeoff weight and the gravity center of the whole aircraft by integrating the weight and the gravity center of each part; based on airplane design parameters and the gravity center of the whole machine, parametric modeling is conducted on all the components, pneumatic calculation is conducted, and a lift-drag analysis result is output; establishing an engine performance model according to the engine type and the engine performance parameters, and generating a power curve and an oil consumption curve based on the model; calculating a plurality of aircraft performance indexes; and performing multi-objective optimization by taking the shape parameters of the wing empennage as design variables and taking a plurality of aircraft performance indexes as optimization objectives, and generating a Pareto optimal solution set containing an optimal value combination of the shape parameters of the wing empennage. The defects of subject conflict and long research and development period caused by the adoption of a serial design process in the existing aircraft design are overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aircraft overall design, and in particular to an aircraft overall optimization design method, device and electronic equipment. BACKGROUND

[0002] Aircraft overall design is a complex engineering that integrates aerodynamics, structural mechanics, propulsion systems and other disciplines. It needs to achieve comprehensive optimization of the design scheme under the premise of ensuring safety and economy. With the development of aviation technology, the performance requirements of aircraft for range, load capacity and fuel efficiency are continuously increasing, and traditional design methods and single-objective optimization techniques have been difficult to meet the demand.

[0003] In the prior art, traditional aircraft design mostly adopts a serial design process. Various discipline teams independently carry out work based on empirical formulas and simplified models, such as aerodynamic design relying on low-precision computational fluid dynamics (CFD) methods or empirical formulas, structural design estimating weight through experience, and propulsion system designing performance based on standard working conditions. Due to the lack of real-time interaction and collaboration, discipline conflicts often occur in the later stage of design, such as conflicts between aerodynamic design and structural weight, and conflicts between propulsion efficiency and fuel consumption, which leads to repeated modification of the scheme and significantly increases the research and development cycle and cost. SUMMARY

[0004] The present application provides an aircraft overall optimization design method, device and electronic equipment to solve the defects of long research and development cycle caused by discipline conflicts in the serial design process of existing aircraft design. The technical solutions of the present application are as follows: In a first aspect, the present application provides an aircraft overall optimization design method, comprising: obtaining aircraft design parameters; the aircraft design parameters include wing and tail shape parameters, engine type, engine performance parameters and cruise altitude; calculating the weight of each component of the aircraft based on the aircraft design parameters, and calculating the maximum take-off weight and the center of gravity of the whole machine by synthesizing the weight and the center of gravity of each component; based on the aircraft design parameters and the center of gravity of the whole machine, parameterizing modeling of the components and performing aerodynamic calculation to output lift-drag analysis results; establishing an engine performance model according to the engine type and the engine performance parameters, and generating a power curve and a fuel consumption curve based on the engine performance model; based on the wing and tail shape parameters, the cruise altitude, the maximum take-off weight, the lift-drag analysis results, the power curve and the fuel consumption curve, calculating a plurality of aircraft performance indicators; The wing and tail shape parameters are taken as design variables, and multiple aircraft performance indexes are taken as optimization targets for multi-objective optimization, to generate a Pareto optimal solution set containing optimal value combinations of the wing and tail shape parameters.

[0005] Optionally, based on the aircraft design parameters and the overall center of gravity, the components are parameterized modeled and aerodynamic calculation is performed to output lift-drag analysis results, including: The aircraft design parameters are parameterized modeled to generate parameterized geometric definitions; A three-dimensional aircraft model is generated based on the parameterized geometric definitions; The overall center of gravity is written into the three-dimensional aircraft model and set as an aerodynamic moment reference point; Calculation condition setting parameters are obtained, and based on the calculation condition setting parameters and the three-dimensional aircraft model, aerodynamic calculation is performed by a vortex lattice method to obtain lift-drag analysis results.

[0006] Optionally, the method further includes outputting static stability analysis results after aerodynamic calculation; the static stability analysis results include a longitudinal static stability margin and a directional stability parameter; The longitudinal static stability margin is a difference between a focus position and a center of gravity position, and the directional stability parameter is determined according to a directional static stability derivative and a roll static stability derivative.

[0007] Optionally, the power curve includes a power-speed curve and a power-altitude curve, the power-speed curve represents a mapping relationship between power and flight speed, and the power-altitude curve represents a mapping relationship between power and cruising altitude; The power is determined according to sea level power, flight speed, cruising altitude and propeller efficiency.

[0008] Optionally, the aircraft performance indexes include cruising speed, range and take-off roll distance; the wing and tail shape parameters include wing area, the lift-drag analysis results include lift coefficient and drag coefficient; Based on the wing and tail shape parameters, the cruising altitude, the maximum take-off weight, the lift-drag analysis results, the power curve and the fuel consumption curve, multiple aircraft performance indexes are calculated, including: A cruising efficiency factor is obtained, cruising altitude corresponding engine output power is determined according to the power-altitude curve, and cruising speed is determined according to the wing area, the cruising altitude, the maximum take-off weight, the lift coefficient, the drag coefficient, the cruising altitude corresponding engine output power and the cruising efficiency factor. Determine the fuel consumption per unit time corresponding to the cruise altitude according to the fuel consumption curve, and determine the range according to the obtained fuel weight, the fuel consumption per unit time and the cruise speed; Obtain the maximum lift coefficient, the taxiing lift coefficient, the taxiing resistance coefficient, the maximum engine power, the runway height, the ground friction resistance coefficient and the wind speed, and determine the take-off taxiing distance according to the wing area, the maximum take-off weight, the maximum lift coefficient, the taxiing lift coefficient, the taxiing resistance coefficient, the maximum engine power, the runway height, the ground friction resistance coefficient and the wind speed.

[0009] Optionally, the wing and tail shape parameters are taken as design variables, and a plurality of aircraft performance indexes are taken as optimization targets for multi-objective optimization to generate a Pareto optimal solution set containing optimal value combinations of the wing and tail shape parameters, including: The wing shape parameter and the tail shape parameter are taken as design variables, and an initial sample set is generated by Latin hypercube sampling; A plurality of aircraft performance indexes are calculated as target values based on each initial sample; The design variables and the target values of the Latin hypercube sampling are paired as a training data set, a pre-constructed neural network proxy model is trained based on the training data set, a mapping relationship from the design variables to the target values is established, and a trained neural network proxy model is obtained; The model accuracy of the trained neural network proxy model is evaluated using a verification sample set, and the training and evaluation process is repeated until the model accuracy meets the preset requirement, and a trained neural network proxy model is obtained; A multi-objective genetic algorithm is adopted, and the trained neural network proxy model is used for optimization iteration to output a Pareto optimal solution set.

[0010] In a second aspect, the present application also provides an aircraft overall optimization design device, including the following sub-modules: A parameter input and control sub-module is used to obtain aircraft design parameters; the aircraft design parameters include wing and tail shape parameters, engine types, engine performance parameters and cruise altitudes; A structure weight and gravity center estimation sub-module is used to calculate the weights of each component of the aircraft based on the aircraft design parameters, and to calculate the maximum take-off weight and the overall gravity center by integrating the weights and gravity centers of each component; An aerodynamic calculation and analysis sub-module is used to parameterize modeling of the components based on the aircraft design parameters and the overall gravity center, and to perform aerodynamic calculation and output lift and drag analysis results; An engine sub-module is used to establish an engine performance model according to the engine types and the engine performance parameters, and to generate power curves and fuel consumption curves based on the engine performance model; a performance calculation sub-module, configured to calculate a plurality of aircraft performance indexes based on the wing and tail shape parameters, the cruise altitude, the maximum take-off weight, the lift-drag analysis result, the power curve and the fuel consumption curve; a multi-objective genetic algorithm optimization sub-module, configured to perform multi-objective optimization with the wing and tail shape parameters as design variables and the plurality of aircraft performance indexes as optimization objectives, to generate a Pareto optimal solution set containing optimal value combinations of the wing and tail shape parameters.

[0011] In a third aspect, the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the aircraft overall optimization design method according to the first aspect when executing the computer program.

[0012] In a fourth aspect, the present application further provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the aircraft overall optimization design method according to the first aspect.

[0013] In a fifth aspect, the present application further provides a computer program product comprising a computer program, and the computer program is executed by a processor to implement the aircraft overall optimization design method according to the first aspect.

[0014] Based on the above technical solutions, the present application has the following beneficial effects compared with the prior art: The aircraft overall optimization design method, device and electronic device provided by the present application integrate the aircraft design parameter acquisition, component weight calculation, aerodynamic calculation, engine performance modeling, performance index calculation and multi-objective optimization in one system. The data of various disciplines can be shared and transmitted in real time, for example, in the aerodynamic calculation, the wing and tail shape parameters and the overall center of gravity are comprehensively considered; in the calculation of the plurality of aircraft performance indexes, the wing area, weight data, lift-drag analysis result and engine performance curve are also comprehensively considered, breaking the independent state of disciplines and realizing real-time interaction and cooperation.

[0015] The traditional serial design often has discipline conflicts in the later design stage due to the independent work of various disciplines, such as the contradiction between aerodynamic design and structural weight, the conflict between propulsion efficiency and fuel consumption, etc. The optimization method fully considers the mutual influence of various disciplines in the design process, and through integrated calculation and analysis, it can timely discover and coordinate to solve potential discipline conflict problems. For example, in the parameterized modeling and aerodynamic calculation stage, the limitation of structural weight on aerodynamic design is considered; in the engine performance modeling and aircraft performance index calculation, the relationship between propulsion efficiency and fuel consumption is considered, thereby avoiding large-scale modification in the later design stage, reducing the repetition in the research and development process, thereby shortening the research and development cycle and reducing the design cost.

[0016] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the description or can be learned by practice of the application. The purposes and other advantages of the application will be realized and attained by the structure particularly pointed out in the description and the appended drawings.

[0017] In order to make the above objectives, features and advantages of the present application more apparent, the following preferred embodiments are specifically described with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0019] Figure 1 is a flowchart of the overall optimization design method of the aircraft provided by the present application.

[0020] Figure 2 is an interface diagram of the parameter input and control submodule provided by the present application Figure 1 .

[0021] Figure 3 is an interface diagram of the parameter input and control submodule provided by the present application Figure 2 .

[0022] Figure 4 is an interface diagram of the structure weight gravity center estimation submodule provided by the present application Figure 1 .

[0023] Figure 5 is an interface diagram of the structure weight gravity center estimation submodule provided by the present application Figure 2 .

[0024] Figure 6 is a calculation flowchart of the structure weight gravity center estimation submodule provided by the present application.

[0025] Figure 7 is an interface diagram of the aerodynamic calculation and analysis submodule provided by the present application.

[0026] Figure 8 is a calculation flowchart of the aerodynamic calculation and analysis submodule provided by the present application.

[0027] Figure 9 is a calculation flowchart of the performance calculation submodule provided by the present application.

[0028] Figure 10 is a schematic diagram of a calculation process of a multi-objective genetic algorithm optimization sub-module provided by the application.

[0029] Figure 11 is a technical roadmap of an aircraft overall optimization design method provided by the application.

[0030] Figure 12 is a structural schematic diagram of an electronic device provided by the application. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions and advantages of the application clearer, the technical solutions in the application will be described clearly and completely below with reference to the drawings in the application. Obviously, the described embodiments are some of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0032] The application provides an aircraft overall optimization design device, comprising the following sub-modules: A parameter input and control sub-module is configured to obtain aircraft design parameters, wherein the aircraft design parameters comprise wing and tail shape parameters, engine types, engine performance parameters and cruise altitudes. A structure weight and gravity center estimation sub-module is configured to calculate the weights of aircraft components based on the aircraft design parameters, and to calculate the maximum take-off weight and the overall gravity center of the aircraft by integrating the weights and gravity centers of the components. An aerodynamic calculation and analysis sub-module is configured to perform parameterized modeling on the components based on the aircraft design parameters and the overall gravity center of the aircraft, and to perform aerodynamic calculation and output lift-drag analysis results. An engine sub-module is configured to establish an engine performance model according to the engine types and the engine performance parameters, and to generate power curves and fuel consumption curves based on the engine performance model. A performance calculation sub-module is configured to calculate a plurality of aircraft performance indexes based on the wing and tail shape parameters, the cruise altitudes, the maximum take-off weight, the lift-drag analysis results, the power curves and the fuel consumption curves. A multi-objective genetic algorithm optimization sub-module is configured to perform multi-objective optimization by taking the wing and tail shape parameters as design variables and the plurality of aircraft performance indexes as optimization objectives, and to generate a Pareto optimal solution set comprising an optimal value combination of the wing and tail shape parameters.

[0033] The aircraft overall optimization design method provided by the application will be described below with reference to the above aircraft overall optimization design device. The aircraft overall optimization design method described below can be correspondingly referred to the above aircraft overall optimization design device.

[0034] The aircraft overall optimization design method provided by the application, referring to Figure 1 as shown, the method comprises the following: S110, obtaining aircraft design parameters.

[0035] The parameter input and control sub-module supports user input of aircraft design parameters, including fuselage size, wing and tail shape parameters (wing shape parameters, tail shape parameters), engine type, engine performance parameters and avionics configuration, etc.

[0036] Referring to Figure 2 as shown, the overall indicators of the parameter input and control sub-module include maximum take-off weight (kg), cruise speed (km / h), fuel weight (kg), pre-installation avionics weight (kg), maximum engine power (kw), engine weight, engine number, payload (kg). Referring to Figure 3 as shown, the above-mentioned fuselage size includes fuselage length, fuselage width, fuselage height, nose length, tail length, etc. The wing shape parameters include wing airfoil (such as NACA4 wing airfoil, NACA5 wing airfoil, NACA6 wing airfoil), wing position (x, y, z), wing area, aspect ratio, tip-to-root ratio, leading edge sweep angle, upwarp angle, geometric twist angle, wing installation angle, average aerodynamic chord, etc. The tail shape parameters include tail area, sweepback angle, tail position, etc. Among them, the tail shape parameters specifically include horizontal tail shape parameters and vertical tail shape parameters. The horizontal tail shape parameters include horizontal tail airfoil (such as NACA4 wing airfoil), horizontal tail position (x, y, z), horizontal tail area, aspect ratio, tip-to-root ratio, leading edge sweep angle, horizontal tail installation angle, horizontal tail capacity, etc. The vertical tail shape parameters include vertical tail airfoil (such as NACA4 wing airfoil), vertical tail position (x, y, z), vertical tail area, aspect ratio, tip-to-root ratio, leading edge sweep angle, vertical tail capacity, etc. The engine type includes turboprop or piston engine, etc. The engine performance parameters include maximum power, cruise altitude, specific fuel consumption (SFC), etc., which are used to construct an engine model.

[0037] This sub-module also provides operation buttons for controlling the operation of the structure weight and gravity estimation sub-module, the aerodynamic calculation and analysis sub-module, the engine sub-module, the performance calculation sub-module and the multi-objective genetic algorithm optimization sub-module, such as starting calculation, resetting parameters, etc.

[0038] S120, calculating the weight of each component of the aircraft based on the aircraft design parameters, and comprehensively calculating the maximum take-off weight and the overall gravity center of the aircraft based on the weight and gravity center of each component.

[0039] Referring to Figure 4As shown, the structural weight and center of gravity estimator module is used to estimate the weight of fuselage, wing, tail (including horizontal tail weight, vertical tail weight), fuel system weight, engine system weight, avionics system weight, electrical system weight, flight control system weight, fuel weight, nose landing gear weight, main landing gear weight, payload and total weight of the aircraft (i.e. the maximum takeoff weight). It is also used to estimate the center of gravity position, including the longitudinal center of gravity of the whole aircraft and the center of gravity of the whole aircraft (MAC). The MAC represents the position of the center of gravity of the whole aircraft in the percentage of the average aerodynamic chord length.

[0040] Referring to Figure 5 As shown, the interface of the structural weight and center of gravity estimator module displays the center of gravity of the fuselage (FL), wing (WMac), horizontal tail (HtMac), vertical tail (VtMac), fuel system (WMac), engine system (FL), avionics system (FL), electrical system (FL), flight control system (FL), fuel (WMac), and also displays the front landing gear weight percentage, front main wheel distance (FL), payload (FL), and total center of gravity (WMac). The total center of gravity is also the above-mentioned center of gravity of the whole aircraft. FL represents the percentage of the center of gravity position of the component relative to the fuselage, for example, if the fuselage input is 0.4, it means that the center of gravity of the fuselage is at the 40% position of the fuselage length. WMac represents the percentage of the center of gravity position of the component relative to the average aerodynamic chord length of the wing, for example, if the wing input is 0.4, it means that the center of gravity of the wing is at the 40% position of the average aerodynamic chord length of the wing. Similarly, HtMac represents the average aerodynamic chord length of the horizontal tail, and VtMac represents the average aerodynamic chord length of the vertical tail.

[0041] Referring to Figure 6 As shown, the structural weight and center of gravity estimator module reads the aircraft shape parameters from the parameter input and control submodule. The aircraft shape parameters are the parameters about the shape in the above-mentioned aircraft design parameters. According to the aircraft shape parameters, the weight of the fuselage, wing, horizontal tail, vertical tail, fuel system, engine system, avionics system, electrical system, flight control system, and landing gear system is estimated, and the total weight of these components is obtained to obtain the maximum takeoff weight. It is judged whether the maximum takeoff weight converges. That is, the preset maximum takeoff weight is read, and the maximum takeoff weight is compared with the preset maximum takeoff weight. If the maximum takeoff weight is less than the preset maximum takeoff weight, it is convergent, and the center of gravity of the whole aircraft is calculated. Otherwise, it is not convergent. The center of gravity data including the maximum takeoff weight and the center of gravity of the whole aircraft are output to the aerodynamic calculation and analysis submodule.

[0042] Specifically, the structure weight gravity estimation operator module estimates the fuselage weight, the wing weight, the horizontal tail weight and the vertical tail weight respectively according to the aircraft design parameters by using empirical regression formula. For the fuel system, the fuel system weight is estimated according to the fuel carrying amount; for the engine system, the engine system weight is determined in combination with the engine type, the number and the auxiliary equipment; the avionics system weight is calculated according to the avionics equipment; the landing gear weight (including the above-mentioned front landing gear weight and the main landing gear weight) is estimated according to the landing gear type and the carrying capacity. The weights of various components are integrated, and the maximum take-off weight and the overall gravity center are calculated in combination with the avionics equipment installation position, the engine installation position, the fuel distribution and other information, and the gravity center data including the maximum take-off weight and the overall gravity center are output to the aerodynamic calculation and analysis submodule. The estimation formula is as follows: In the above formula, is the wing weight, is the wing weight correction coefficient, the value range of which is 0.9-1.1. is a function of the maximum take-off weight , the use overload , the wing area , the wing aspect ratio , the wing leading edge sweep angle , the flight speed , the wing tip ratio , the wing average relative thickness . The functions involved in the present application can be set according to actual needs by those skilled in the art, or reference can be made to the related functions in the prior art, which are not specifically limited here.

[0043] In the above formula, is the horizontal tail weight, is the horizontal tail weight correction coefficient, the value range of which is 0.9-1.1, is a function of the maximum take-off weight , the use overload , the flight speed , the horizontal tail area , the wing average relative thickness , the horizontal tail leading edge sweep angle , the wing aspect ratio , the wing leading edge sweep angle , the horizontal tail tip ratio .

[0044] In the above formula, is the vertical tail weight, This is the correction factor for the weight of the vertical tail. The value ranges from 0.9 to 1.3. It's about the maximum takeoff weight. Overload Flight speed Vertical tail area wing average relative thickness , sweep angle of the leading edge of the vertical tail Wing aspect ratio , drooping tail root ratio The function.

[0045] In the above formula For the weight of the fuselage, This is a correction factor for the fuselage weight. The value ranges from 0.9 to 1.1. It's about flight speed. , flat tail moment arm , fuselage structural width fuselage structure height Surface area of ​​fuselage The function.

[0046] In the above formula, For the weight of the engine system, This is the engine system weight correction factor. The value ranges from 0.9 to 1.1. It's about the number of engines. Engine weight Engine power The function.

[0047] In the above formula, For the weight of the fuel system, This is a weight correction factor for the fuel system. The value ranges from 0.9 to 1.1. It's about the number of engines. fuel weight The function.

[0048] In the above formula, For the weight of the avionics system, This is the weight correction factor for the avionics system. The value ranges from 0.9 to 1.1. It concerns the weight of avionics before installation. The function.

[0049] In the above formula, For the weight of the electrical system, This is a weight correction factor for the electrical system. The value ranges from 0.9 to 1.1. It's about the weight of the fuel system. avionics system weight The function.

[0050] In the above formula, For the weight of the flight control system, This is the weight correction factor for the flight control system. The value ranges from 0.9 to 1.1. It's about the fuselage length. Wingspan Overload Maximum takeoff weight The function.

[0051] In the above formula, For the weight of the landing gear system, This is a weight correction factor for the landing gear system. The value ranges from 0.9 to 1.1. It's about the maximum takeoff weight. The function.

[0052] = + + + + + + + + + In the above formula, For maximum takeoff weight, For wing weight, For the weight of the flat-tail, The weight of the vertical tail. For the weight of the fuselage, For the weight of the engine system, For the weight of the fuel system, For the weight of the avionics system, For the weight of the electrical system, The weight of the flight control system, The weight of the landing gear system.

[0053] The center of gravity of the whole machine is calculated by weighting the weight of each component and its geometric position (such as the position of the wing, the position of the engine), to ensure the stability of flight. For details, please refer to the description in the prior art, which will not be repeated here.

[0054] S130, based on the aircraft design parameters and the center of gravity of the whole machine, the components are parameterized modeled and aerodynamic calculation is performed to output the lift-drag analysis results.

[0055] Use special software (such as OpenVSP) to define the wing, fuselage and other components as adjustable parameters (such as wing span, sweep angle, etc.) geometric model. The vortex lattice method is used to calculate the lift coefficient and drag coefficient, and the variation curve of the lift-drag with the calculation condition setting parameters such as angle of attack is output as the lift-drag analysis result.

[0056] S140, establishing an engine performance model according to the engine type and the engine performance parameters, and generating a power curve and a fuel consumption curve based on the engine performance model.

[0057] According to the type and performance parameters of the engine, a mathematical model of the engine is established. The model can describe the relationship between the performance indicators such as power and fuel consumption rate of the engine under different working conditions and parameters such as cruising altitude and flight speed.

[0058] Using the established engine performance model, the power curve and the fuel consumption curve of the engine are generated by numerical calculation and output to the performance calculation submodule. The power curve reflects the variation law of the engine power with the flight speed and the cruising altitude; the fuel consumption curve represents the fuel consumption rate of the engine under different working conditions. These characteristic curves are important basis for evaluating the performance of the engine and calculating the performance of the aircraft. The power curve includes a power-speed curve and a power-altitude curve, the power-speed curve represents the mapping relationship between power and flight speed, and the power-altitude curve represents the mapping relationship between power and cruising altitude. The power is determined according to the sea level power, flight speed, cruising altitude and propeller efficiency.

[0059] The expression of the power-altitude curve is as follows: In the above formula, is the engine output power corresponding to the cruising altitude, is the sea level power, is the cruising altitude, is the critical altitude.

[0060] The expression of the power-speed curve is as follows: In the above formula, is the power corresponding to the flight speed, is the propeller efficiency, is the power at sea level, is the flight speed, is the flight resistance, is a function of the power at sea level , the flight speed , and the propeller efficiency . wherein is the air density, is the wing area.

[0061] The present application determines the power-speed curve in combination with the power at sea level, the flight speed, and the propeller efficiency, taking into account the actual working conditions of the engine at different flight speeds. The power at sea level is a basic performance parameter of the engine, and the flight speed and the propeller efficiency reflect the working environment and working efficiency of the engine in actual flight. This comprehensive calculation method can more accurately reflect the real performance of the engine in the cruising state. This makes the aircraft design more close to the actual use requirements, and improves the rationality and reliability of the design.

[0062] The expression of the fuel consumption curve is as follows: In the above formula, is the fuel consumption per unit time, BSFC is the specific fuel consumption, is the engine output power corresponding to the cruising altitude, is the cruising efficiency factor.

[0063] S150, based on the wing tail shape parameter, the cruising altitude, the maximum take-off weight, the lift-drag analysis result, the power curve and the fuel consumption curve, a plurality of aircraft performance indicators are calculated.

[0064] The selection of the cruising altitude usually takes into account factors such as flight mission requirements, atmospheric conditions, engine performance, etc. For example, in order to obtain better fuel economy, long-distance passenger aircraft usually choose to fly at a higher cruising altitude, because the air pressure is lower at high altitude, the air is thin, and the aircraft receives less resistance.

[0065] According to the obtained cruising altitude, in combination with the wing area (affecting the lift and drag characteristics of the aircraft), the maximum take-off weight (determining the power and fuel consumption required by the aircraft), the lift-drag analysis result (providing the aerodynamic force data of the aircraft in different flight states), the power curve and the fuel consumption curve (reflecting the performance of the engine), the performance indicators of the aircraft are calculated, such as cruising speed, range, endurance time, take-off roll distance, fuel consumption rate, etc. For example, the range can be calculated by integrating the fuel consumption and speed relationship of the aircraft in different flight stages.

[0066] S160, taking the wing tail shape parameters as design variables, and taking multiple aircraft performance indicators as optimization objectives to perform multi-objective optimization, and generating a Pareto optimal solution set containing optimal value combinations of the wing tail shape parameters.

[0067] According to the design requirements and flight task requirements of the aircraft, the aircraft performance indicators to be optimized are determined, such as maximizing the range, maximizing the cruising speed, and minimizing the take-off roll distance, etc. Genetic algorithm is adopted to search for multiple conflicting optimization objectives through the selection, crossover and mutation mechanisms in the natural evolution process, and finally the Pareto optimal solution set is obtained. Each solution in the solution set is a non-dominated solution - that is, it cannot improve one optimization objective without compromising other objectives. Each solution represents a relatively balanced design scheme under different trade-off relationships between objectives.

[0068] Through analysis and evaluation of the Pareto optimal solution set, in combination with the constraint boundary conditions of the design (such as the upper and lower bounds of each parameter of the design variables), the most suitable design scheme is selected to guide the detailed design and further optimization of the aircraft.

[0069] In the traditional serial design, each discipline team such as aerodynamics, structure, and propulsion works independently, and lacks real-time interaction. The aircraft overall optimization design method provided by the present application integrates aircraft design parameter acquisition, component weight calculation, aerodynamic calculation, engine performance modeling, performance indicator calculation, and multi-objective optimization into one system. The data of each discipline can be shared and transmitted in real time, for example, in aerodynamic calculation, the wing shape parameters and the center of gravity of the whole machine (related to structure design) are integrated; when calculating multiple aircraft performance indicators, the wing area, weight data, lift-drag analysis results, and engine performance curves are integrated, breaking the independent state between disciplines and realizing real-time interaction and collaboration.

[0070] The traditional serial design often appears subject conflicts in the later design stage due to the independent work of each subject, such as the contradiction between aerodynamic design and structural weight, the conflict between propulsion efficiency and fuel consumption, etc. The optimization method fully considers the mutual influence of each subject in the design process, and can timely discover and coordinate to solve potential subject conflict problems through integrated calculation and analysis. For example, in the parameterized modeling and aerodynamic calculation stage, the limitation of structural weight on aerodynamic design is considered; in the engine performance modeling and aircraft performance index calculation, the relationship between propulsion efficiency and fuel consumption is considered, thereby avoiding large-scale modification in the later design stage and reducing the repetition in the research and development process.

[0071] In the traditional serial design, aerodynamic design relies on low-precision CFD methods or empirical formulas, structural design estimates weight through experience, and propulsion system designs performance based on standard working conditions. These simplified methods lead to insufficient accuracy of the design results. In the optimization method, aerodynamic calculation uses advanced CFD software to perform accurate calculation in combination with the three-dimensional geometric model generated by parameterized modeling, which can output more accurate lift and drag analysis results; the weight of each component is calculated based on scientific methods according to the aircraft design parameters, and the maximum take-off weight and the center of gravity of the whole machine are calculated by integrating the weight and center of gravity of each component; a detailed engine performance model is established according to the type and performance parameters of the engine to generate power curves and fuel consumption curves, providing reliable data support for aircraft performance index calculation. The high-precision analysis method makes the design results more consistent with the actual situation, reducing the modification and adjustment caused by inaccurate design in the later stage.

[0072] The traditional serial design often focuses on the optimization of a single subject or a single performance index, and it is difficult to achieve global optimization of the overall performance of the aircraft. The optimization method takes the aircraft performance index (such as cruise speed, range, take-off roll distance, etc.) as the optimization target, and uses a multi-objective genetic optimization algorithm for optimization. The method balances and coordinates between multiple contradictory optimization targets, finds a set of optimal solutions, i.e. the Pareto optimal solution set, so as to achieve global optimization of the aircraft design scheme in multiple targets and improve the overall performance of the aircraft.

[0073] In an optional embodiment, the aerodynamic calculation and analysis submodule undertakes the key task of aerodynamic performance calculation and analysis in the entire aircraft design optimization system. It completes parameterized modeling of each component of the aircraft, aerodynamic calculation, and result analysis and processing by interacting with multiple submodules. The parameterized modeling of each component based on the aircraft design parameters and the center of gravity of the whole machine, and the output of lift and drag analysis results in S130 include: S1301, parameterized modeling of the aircraft design parameters to generate parameterized geometric definitions.

[0074] S1302, generating a three-dimensional model of the aircraft based on the parameterized geometric definitions.

[0075] S1303, write the whole machine gravity center to the aircraft three-dimensional model, and set it as the aerodynamic moment reference point.

[0076] S1304, obtain calculation working condition setting parameters, and based on the calculation working condition setting parameters and the aircraft three-dimensional model, obtain lift-drag force analysis results through aerodynamic calculation by vortex lattice method.

[0077] Referring to Figure 7 As shown in the figure, the interface of the aerodynamic calculation and analysis submodule displays calculation working condition setting parameters, including angle of attack, sideslip angle, Mach number, Reynolds number, and moment point (x, y, z). Through aerodynamic calculation and result analysis, one or more of the following is output: lift curve, drag curve, polar curve, lift-drag ratio, pitch moment, roll moment, yaw moment, and static stability.

[0078] Referring to Figure 8 As shown in the figure, the aerodynamic calculation and analysis submodule reads aircraft shape parameters (i.e. parameters about shape in aircraft design parameters) from the parameter input and control submodule, such as fuselage parameters (including fuselage length, fuselage width, fuselage height, nose length, and tail length), wing parameters (including wing airfoil, wing position, wing area, aspect ratio, tip-to-root ratio, leading edge sweep angle, upwash angle, geometric twist angle, and wing installation angle), tail plane shape parameters (including tail plane airfoil, tail plane position, tail plane area, aspect ratio, tip-to-root ratio, leading edge sweep angle, and tail plane installation angle), and vertical tail shape parameters (including vertical tail airfoil, vertical tail position, vertical tail area, aspect ratio, and tip-to-root ratio). The above components are parameterized modeled (including fuselage parameterized modeling, wing parameterized modeling, tail plane parameterized modeling, and vertical tail parameterized modeling) using AngleScript scripting language, geometric parameters are defined, and parameterized geometric definitions are obtained. For example, a wing generates a parameterized surface through airfoil coordinate points, span length, and chord length distribution. Tail, fuselage, and other components use similar methods to control shape, position, and scale ratio.

[0079] At the same time, the submodule receives whole machine gravity center data from the structure weight gravity center estimation submodule. The whole machine gravity center is an important reference point for aircraft aerodynamic moment calculation. Setting the whole machine gravity center as the aerodynamic moment reference point can accurately calculate the balance and control characteristics of the aircraft under the action of aerodynamic force, and provide a basis for subsequent aerodynamic stability analysis.

[0080] In addition, calculation working condition setting parameters, including angle of attack, sideslip angle, Mach number, and Reynolds number, also need to be received. These parameters define the flight state and environmental conditions of the aircraft, and different calculation working conditions will have a significant impact on the aerodynamic performance of the aircraft. For example, the change of angle of attack will directly affect the lift and drag of the aircraft, and the Mach number is related to the compressibility effect of the aircraft.

[0081] Using the received aircraft design parameters, parametric modeling of various aircraft components is performed using the AngleScript scripting language. AngleScript is a scripting language that can interact with OpenVSP programs. In the script, variables and functions are defined to describe the geometry and parametric relationships of each component. This script allows for the dynamic generation of geometric models of each aircraft component based on the input parameters, achieving parametric and automated modeling.

[0082] After completing the parametric modeling script, the OpenVSP program is called to execute the script. OpenVSP (OpenVehicle Sketch Pad) is an open-source parametric aircraft design software that can read AngleScript scripts and generate corresponding 3D aircraft model files (.vsp3 format), outputting a .vsp3 model. The .vsp3 model contains detailed geometric information of each aircraft component, providing an accurate geometric model foundation for subsequent aerodynamic calculations.

[0083] Based on the received calculation parameters, the AngleScript scripting language is used again to generate a .vspscript script file. This script file is mainly used to set the relevant parameters for aerodynamic calculations and to call the vortex lattice method for calculation. The generated .vspscript script file is executed by calling the OpenVSP program to start the vortex lattice method aerodynamic calculation method. The vortex lattice method is an aerodynamic calculation method based on the element method. It discretizes the surfaces of various aircraft components into multiple small elements (vortex lattices), and arranges horseshoe vortices on each vortex lattice to simulate the vortex distribution on the object's surface. By satisfying the non-penetrating boundary conditions of the object's surface, a set of linear equations about the intensity of each vortex lattice is established. Solving this set of equations yields the pressure distribution on the aircraft surface, and then the aerodynamic forces such as lift and drag experienced by the aircraft can be calculated.

[0084] The calculated lift and drag data are analyzed for lift and drag. Curves depicting lift and drag as a function of parameters such as angle of attack and Mach number are plotted to analyze the aircraft's lift and drag characteristics. By analyzing the lift and drag data, the aerodynamic performance of the aircraft under different flight conditions can be evaluated, providing a basis for the optimized design of the aircraft.

[0085] The lift analysis expression is as follows: In the above formula, The slope of the lift line, The lift coefficient, For the angle of attack, This represents the partial derivative.

[0086] The resistance analysis expression is as follows: In the above formula, is the drag coefficient, is the zero-lift drag coefficient, is the induced drag factor, is the correction drag coefficient.

[0087] The present application allows rapid changes in multiple features of the aircraft shape by adjusting a few key parameters through parameterized modeling. For example, by modifying parameters such as wing area, aspect ratio, and tip-to-root ratio, wing geometry definitions of different sizes and shapes can be quickly generated without the need to redesign every detail from scratch, greatly improving design efficiency and facilitating the exploration and comparison of various design options. The parameterized geometry definition establishes an internal relationship, so that the shape parameters of each part of the aircraft are mutually constrained and coordinated. When a parameter is modified, other parts related to it will automatically adjust to ensure the rationality and consistency of the entire aircraft shape design, avoiding local mismatch problems caused by manual modification. The parameterized model provides convenience for subsequent optimization algorithms. Optimization algorithms can find the optimal design scheme under given constraints, such as minimum drag, maximum lift, or optimal lift-drag ratio, by adjusting these parameters, thereby improving the overall performance of the aircraft.

[0088] The three-dimensional model of the aircraft provides a shared platform for various disciplines of aircraft design. Professionals from different disciplines, such as aerodynamics, structure, etc., can work on the same model, obtain the required information, and perform corresponding analysis and design. This collaborative working method helps to improve the overall efficiency of the design team and ensures that the designs of various disciplines are coordinated to meet the comprehensive performance requirements of the aircraft. The center of gravity of the whole machine is an important reference point for the moment generated by aerodynamic forces during flight. Accurately writing the center of gravity position into the three-dimensional model of the aircraft and setting it as the aerodynamic moment reference point can ensure the accuracy of moment calculation in aerodynamic calculation. Combined with the three-dimensional model of the aircraft and the calculation condition parameters, the vortex lattice method can quickly and accurately perform aerodynamic calculation, providing important performance data for aircraft design. By obtaining different calculation condition design parameters, the aerodynamic performance of the aircraft under various flight conditions can be comprehensively analyzed. This helps to understand the lift-drag characteristics and aerodynamic stability of the aircraft under different conditions, providing rich data support for the design optimization and performance evaluation of the aircraft, ensuring that the aircraft can meet the performance requirements under various expected flight conditions. The vortex lattice method-based aerodynamic calculation has the advantages of low cost and short cycle. During the design process, multiple aerodynamic calculations can be quickly performed to evaluate and compare different design options, and design parameters can be adjusted in a timely manner, thereby shortening the design cycle of the aircraft.

[0089] After the aerodynamic calculation is completed, the calculation results are analyzed for static stability. Static stability is the ability of an aircraft to automatically return to its original flight state after being disturbed by external disturbances, including longitudinal static stability and lateral static stability. The method further includes outputting static stability analysis results after the aerodynamic calculation is performed. The static stability analysis results include a longitudinal static stability margin and a lateral stability parameter. The longitudinal static stability margin is the difference between the focus position and the center of gravity position, and the lateral stability parameter is determined according to a directional static stability derivative and a roll static stability derivative.

[0090] In the above formula, is the longitudinal static stability margin, is the focus position, is the center of gravity position; is the lateral stability parameter, is the directional static stability derivative, is the roll static stability derivative.

[0091] The calculated longitudinal static stability margin and lateral stability parameter are output as static stability analysis results. These results can be directly output in numerical form or displayed in chart form, such as drawing a curve of the longitudinal static stability margin with respect to the angle of attack or the Mach number, and displaying the distribution of the lateral stability parameter under different flight conditions. If the static stability analysis results meet the preset requirements, the generated lift-drag analysis results and static stability analysis results are output to the performance calculation submodule, providing an important basis for the optimization design and performance evaluation of the aircraft. If the static stability analysis results do not meet the preset requirements, the aircraft design parameters need to be adjusted, and the aerodynamic calculation and analysis need to be performed again.

[0092] This invention ensures sufficient longitudinal static stability of the aircraft during flight by accurately calculating the longitudinal static stability margin. When the aircraft is subjected to pitch disturbances (such as sudden changes in airflow), a positive longitudinal static stability margin allows the aircraft to automatically return to its original pitch attitude, avoiding dangerous situations such as pitch divergence and thus improving flight safety. Determining lateral stability parameters helps assess the aircraft's static stability in the heading and roll directions. Reasonable lateral stability prevents excessive yaw or roll during sideslip, ensuring stable lateral flight and further improving flight safety. Static stability analysis results can guide the optimization of aircraft design parameters. For example, if the longitudinal static stability margin is too large, it may lead to decreased aircraft controllability. In this case, the parameters of the wing or horizontal stabilizer can be adjusted appropriately, such as changing the wing sweep angle or the horizontal stabilizer area, to adjust the focal point position and bring the longitudinal static stability margin within a suitable range. Similarly, for aircraft with unsatisfactory lateral stability parameters, lateral stability can be improved by adjusting parameters such as the vertical tail or the dihedral angle of the wing, thereby enhancing the overall aerodynamic performance of the aircraft. During the aircraft design process, aerodynamic calculations and static stability analysis allow for the assessment and prediction of the aircraft's static stability characteristics at an early stage, avoiding extensive wind tunnel and flight testing. This reduces the number of tests and testing costs, shortens the design cycle, and accelerates the aircraft's development. Static stability analysis can identify static stability issues in the design phase, allowing designers to adjust the design scheme promptly based on the analysis results. This avoids design changes and rework due to static stability problems during later testing or production, thereby reducing design costs and improving design efficiency.

[0093] In an optional embodiment, the aircraft performance indicators include cruise speed, range, and takeoff distance; the wing shape parameters include wing area; and the lift-drag analysis results include lift coefficient and drag coefficient. (Refer to...) Figure 9 As shown, the performance calculation submodule receives aerodynamic parameters from the aerodynamic calculation and analysis submodule, maximum takeoff weight and center of gravity data from the structural weight and center of gravity estimation submodule, and power and fuel consumption curves from the engine submodule. It also receives data from each submodule, including wing area and cruise altitude from the parameter input and control submodule, maximum takeoff weight from the structural weight and center of gravity estimation submodule, lift and drag data (including lift coefficient and drag coefficient) from the aerodynamic calculation and analysis submodule, and power and fuel consumption data (i.e., fuel consumption curve) from the engine submodule. Based on this data, it calculates the aircraft's cruise speed, range, and takeoff distance, and outputs these parameters to the multi-objective genetic algorithm optimization submodule.

[0094] The S150 described above is based on the wing tail shape parameter, the cruise altitude, the maximum take-off weight, the lift-drag analysis result, the power curve and the fuel consumption curve, and a plurality of aircraft performance indicators are calculated, including: S1501, obtain a cruise efficiency factor, determine the engine output power corresponding to the cruise altitude according to the power-altitude curve, and determine the cruise speed according to the wing area, the cruise altitude, the maximum take-off weight, the lift coefficient, the drag coefficient, the engine output power corresponding to the cruise altitude and the cruise efficiency factor.

[0095] In the above formula, is the cruise speed, is a function of the wing area , the cruise altitude , the maximum take-off weight , the lift coefficient , the drag coefficient , the engine output power corresponding to the cruise altitude and the cruise efficiency factor .

[0096] S1502, determine the fuel consumption per unit time corresponding to the cruise altitude according to the fuel consumption curve, and determine the range according to the obtained fuel weight, the fuel consumption per unit time and the cruise speed.

[0097] In the above formula, is the range, is a function of the fuel weight , the fuel consumption per unit time and the cruise speed .

[0098] S1503, obtain the maximum lift coefficient, the taxiing lift coefficient, the taxiing drag coefficient, the maximum engine power, the runway height, the ground friction drag coefficient and the wind speed, and determine the take-off taxiing distance according to the wing area, the maximum take-off weight, the maximum lift coefficient, the taxiing lift coefficient, the taxiing drag coefficient, the maximum engine power, the runway height, the ground friction drag coefficient and the wind speed.

[0099] In the above formula, is the take-off taxiing distance.

[0100] is a function of the wing area , the maximum take-off weight , the maximum lift coefficient , the lift coefficient of the takeoff , the drag coefficient of the takeoff , the maximum power of the engine , the runway height , the ground friction drag coefficient , and the wind speed.

[0101] The present application comprehensively considers the wing area, the cruising altitude, the maximum takeoff weight, the lift coefficient, the drag coefficient, the engine output power corresponding to the cruising altitude, and the cruising efficiency factor, and can more comprehensively and accurately reflect the force condition and energy conversion relationship of the aircraft in the cruising state. For example, the wing area affects the generation of lift and drag, the cruising altitude changes the air density and then affects the size of the lift and drag, the cruising altitude also affects the output power of the engine, the maximum takeoff weight determines the power required by the aircraft, the lift coefficient and the drag coefficient are directly related to the aerodynamic performance of the aircraft, the engine output power provides the power source, and the cruising efficiency factor considers the efficiency loss of the engine in the actual working process. Through comprehensive calculation of these factors, the cruising speed obtained is closer to the real situation of the aircraft in actual flight.

[0102] The unit time fuel consumption corresponding to the cruising altitude is determined based on the fuel consumption curve, and the range is calculated in combination with the obtained fuel weight and cruising speed. This calculation method considers the relationship between fuel consumption and flight time and speed. The fuel consumption curve is obtained by an accurate engine model, which can accurately reflect the fuel consumption characteristics of the engine under different working conditions. At the same time, in combination with the key parameter of cruising speed, the calculation of the range is more scientific and reasonable, which provides a reliable basis for the range planning of the aircraft.

[0103] In the calculation of the takeoff roll distance, multiple parameters such as the maximum lift coefficient, the takeoff lift coefficient, the takeoff drag coefficient, the maximum power of the engine, the runway height, the ground friction drag coefficient, and the wind speed are obtained, and factors such as the wing area and the maximum takeoff weight are comprehensively considered. The maximum lift coefficient determines the aircraft takeoff speed, the takeoff lift coefficient and the ground friction drag coefficient determine the friction drag of the aircraft in the takeoff process, the takeoff drag coefficient reflects the air resistance of the aircraft, the maximum power of the engine provides the power required for takeoff, the runway height affects the air density and the engine performance, and the wind speed directly affects the takeoff movement of the aircraft. By comprehensively considering these factors, the takeoff distance of the aircraft from static acceleration to takeoff speed can be more accurately calculated.

[0104] ​Since wing shape parameters such as wing area play an important role in calculating performance indicators such as cruise speed, range, and take-off roll distance, analyzing the impact of different wing areas on performance indicators can optimize wing design. For example, increasing wing area may increase lift but also increase drag, affecting cruise speed and range. By calculating performance indicators under different wing areas, the optimal range of wing area can be found to achieve a good balance in various performance indicators. Considering the impact of wing area, engine performance, cruise altitude, maximum take-off weight, and other factors on aircraft performance indicators can help optimize the overall layout of the aircraft. For example, when determining the center of gravity position, landing gear layout, and other aspects, these factors need to be considered in terms of their impact on performance indicators such as take-off roll distance and cruise stability. Through reasonable overall layout optimization, the comprehensive performance of the aircraft can be improved, and the design and operating costs of the aircraft can be reduced.

[0105] In an optional embodiment, the wing and tail shape parameters are used as design variables, and multiple aircraft performance indicators are used as optimization objectives for multi-objective optimization to generate a Pareto optimal solution set containing the optimal combination of wing and tail shape parameters, including: S1601, receive wing shape parameters and tail shape parameters as design variables, and generate an initial sample set through Latin hypercube sampling.

[0106] Referring to Figure 10 , the multi-objective genetic algorithm optimization submodule first receives the wing shape parameters and tail shape parameters (including horizontal tail shape parameters and vertical tail shape parameters) from the parameter input and control submodule. These parameters together form the design variable vector X, which has the following mathematical expression: In the above formula, is the wing longitudinal position, is the wing area, is the wing aspect ratio, is the wing tip ratio, is the wing leading edge sweep angle, is the horizontal tail longitudinal position, is the horizontal tail area, is the horizontal tail aspect ratio, is the horizontal tail tip ratio, is the horizontal tail leading edge sweep angle, is the horizontal tail mounting angle, is the vertical tail longitudinal position, is the vertical tail area, is the vertical tail aspect ratio, is the vertical tail tip ratio, is the vertical tail leading edge sweep angle.

[0107] The design variable vector X is taken as input, and the Latin hypercube sampling method is used to generate initial samples. Latin hypercube sampling is a stratified sampling technique that can divide the design space into multiple subintervals and randomly sample within each subinterval, ensuring that the samples are uniformly distributed throughout the design space. This allows for more comprehensive coverage of possible design solutions, providing a rich data foundation for subsequent model training and optimization.

[0108] S1602、Based on each initial sample, a plurality of aircraft performance indicators are calculated as target values.

[0109] For each initial sample as input parameter, it is calculated in turn through the following several submodules, and finally the target value is output by the performance calculation submodule: Structure weight and gravity estimation submodule: according to the shape parameters of the wings and tail, the maximum take-off weight and overall gravity of the aircraft are estimated. The maximum take-off weight and overall gravity will affect the flight performance of the aircraft, for example, excessive maximum take-off weight will increase the take-off weight of the aircraft, thereby affecting the take-off roll distance and range; the change of overall gravity position will affect the stability and maneuverability of the aircraft.

[0110] Aerodynamic calculation and analysis submodule: using CFD and other methods, the aerodynamic characteristics of the aircraft are analyzed and calculated. By solving the fluid mechanics equation, the lift coefficient, drag coefficient and other aerodynamic parameters of the aircraft under different flight conditions are obtained. These parameters are important basis for calculating the flight performance of the aircraft, for example, the lift coefficient determines the size of the lift of the aircraft, and the drag coefficient affects the flight resistance and fuel consumption of the aircraft.

[0111] Engine submodule: considering the performance characteristics of the engine, such as thrust, fuel consumption rate, etc. The performance of the engine directly affects the flight speed, range and take-off performance of the aircraft. For example, the thrust of the engine determines the acceleration ability and cruising speed of the aircraft, and the fuel consumption rate affects the range and operating cost of the aircraft.

[0112] Performance calculation submodule: the calculation results of the structure weight and gravity estimation submodule, aerodynamic calculation and analysis submodule and engine submodule are integrated to calculate the performance indicators of the aircraft, which are taken as target values. Optimization target F Mathematically expressed as: F In the above formula, represents maximizing cruising speed, represents maximizing range, represents minimizing take-off roll distance.

[0113] These three objectives often have contradictory relationships. For example, pursuing maximum cruising speed may increase aircraft drag, thereby reducing range; reducing takeoff distance may require greater engine thrust, which in turn affects fuel consumption and range. Therefore, a multi-objective optimization method is needed to find a balance between these objectives.

[0114] S1603. Pair the design variables and target values ​​of Latin hypercube sampling as a training dataset, train a pre-built neural network proxy model based on the training dataset, establish the mapping relationship between design variables and target values, and obtain the trained neural network proxy model.

[0115] The design variable vector X obtained from Latin hypercube sampling is paired with its corresponding target value to form a training dataset. This training dataset is then used to construct and train a neural network surrogate model, enabling it to learn the mapping relationship between "design variables → target values". The neural network surrogate model is a mathematical model that establishes a mapping relationship between input and output by simulating the working method of neurons in the human brain. During training, the neural network continuously adjusts its internal weights and bias parameters to make the model's predicted output as close as possible to the actual target value, thereby establishing the mapping relationship between design variables and target values. Through training, the neural network surrogate model can quickly predict the target value corresponding to the design variable, avoiding repeated calls to complex computational submodules, thus greatly improving optimization efficiency.

[0116] S1604. Use the validation sample set to evaluate the model accuracy of the trained neural network agent model, repeat the training and evaluation process until the model accuracy meets the preset requirements, and obtain the trained neural network agent model.

[0117] The trained neural network surrogate model is validated using a certain sample size. Validation samples are a set of samples independently drawn from the design space and are not involved in the model training process. The model's accuracy is evaluated by comparing the error between the model's predicted output on the validation samples and the actual target value, using metrics such as mean squared error (MSE) and mean absolute error (MAE).

[0118] If the model's accuracy does not meet the requirements, the neural network's structure or parameters need to be adjusted, and retraining and validation re-performed, or Latin hypercube sampling re-performed, until the model's accuracy meets the requirements, thus obtaining a trained neural network surrogate model. Only neural network surrogate models that meet the accuracy requirements can be used for subsequent multi-objective genetic algorithm optimization design to ensure the reliability and accuracy of the optimization results.

[0119] S1605. Using a multi-objective genetic algorithm, perform optimization iterations based on the trained neural network surrogate model to output the Pareto optimal solution set.

[0120] After the neural network surrogate model meets the accuracy requirements, a multi-objective genetic algorithm is used for optimization design. The multi-objective genetic algorithm is an optimization algorithm that simulates the process of biological evolution. It searches for optimal solutions that satisfy multiple objective functions in the solution space through selection, crossover, and mutation operations. In each iteration, the algorithm evaluates and selects individuals in the current population based on the predicted results of the neural network surrogate model to generate a new generation of population. After multiple iterations, the algorithm converges to a set of non-dominated solutions, i.e., the Pareto optimal solution set. These solutions achieve a good balance between multiple objective functions. The iteration process of the multi-objective genetic algorithm mainly includes the following steps: (1) Population initialization: Generate an initial population within the constraint boundary conditions of the design variable vector X. The constraint boundary conditions are expressed mathematically as: In the above formula, is each parameter in the design variable vector X, is the lower bound of the corresponding parameter, is the upper bound of the corresponding parameter.

[0121] (2) Fitness calculation: Calculate the fitness value of each individual using the trained neural network surrogate model. The fitness value reflects the comprehensive performance of the individual on multiple objective functions, which is determined according to the optimization objective F(X). For example, for maximizing the cruise speed max(V cru ) and maximizing the range max(R), the fitness value can take its positive value; for minimizing the take-off roll distance min(L TOR ), the fitness value can take the negative value of its inverse to unify the maximum optimization.

[0122] (3) Non-dominated sorting: Sort the individuals in the population according to their fitness values. The purpose of non-dominated sorting is to divide the individuals in the population into different non-dominated layers. Individuals in the same non-dominated layer do not dominate each other, while individuals in higher non-dominated layers are superior to individuals in lower non-dominated layers. Through non-dominated sorting, the superiority and inferiority of each individual in multi-objective optimization can be determined.

[0123] (4) Genetic operations: including selection, crossover, and mutation operations. Selection operation selects excellent individuals as parents from the current population based on the non-dominated sorting results and fitness values of the individuals to generate the next generation of population. Crossover operation generates new individuals by exchanging part of the genes of parent individuals, increasing the diversity of the population. Mutation operation further explores the design space by randomly changing some genes of individuals to avoid falling into local optimal solutions.

[0124] (5) New generation population generation: after genetic operations such as selection, crossover and mutation, a new generation of population is generated. The new generation of population inherits the excellent characteristics of the last generation of population, and at the same time introduces new gene combinations through mutation operation, which helps to search for better solutions in subsequent iterations.

[0125] (6) Repeat the iteration process of "population initialization→fitness calculation→non-dominated sorting→genetic operation→new generation population" above until the preset convergence condition (such as reaching the maximum number of iterations or the convergence condition of the solution) is met. Finally, the Pareto front solution set is searched out, that is, a set of non-dominated solutions that achieve the best balance between multiple objective functions. These solutions achieve a good balance between multiple objective functions, and are output to the result display and output submodule.

[0126] Traditional aircraft performance optimization methods usually require a large number of numerical simulation calculations, such as computational fluid dynamics (CFD), which are time-consuming and consume a lot of computing resources. Using a neural network proxy model can quickly predict the target value corresponding to the design variable, avoiding repeated numerical simulation calculations and greatly reducing the calculation cost and time. The present application uses a multi-objective genetic algorithm to optimize and iterate on the basis of a neural network proxy model, which can quickly search for a Pareto optimal solution set. Compared with directly optimizing in the original design space, this method can significantly speed up the optimization process and improve the optimization efficiency.

[0127] The Latin hypercube sampling method ensures the uniform distribution of the initial sample set in the design space, which can more comprehensively cover possible design schemes. This provides rich data for the training of the neural network proxy model, enabling the model to better capture the complex relationship between design variables and target values, so as to explore more potential optimal solutions in the optimization process. The multi-objective genetic algorithm can consider multiple performance indicators simultaneously and search for a set of Pareto optimal solutions. These solutions achieve a good balance between each objective function, providing more selection space. By repeatedly training and evaluating the neural network proxy model, its accuracy is ensured to meet the accuracy requirements. The trained neural network proxy model can accurately predict the target value corresponding to the design variable, providing a reliable basis for multi-objective optimization. The use of an independent validation sample set to evaluate the accuracy of the neural network proxy model avoids overfitting and improves the model's generalization ability. This validation and evaluation mechanism ensures the accuracy of the neural network proxy model, making the optimization results based on the proxy model highly reliable. At the same time, the setting of the constraint boundary condition ensures the feasibility of the design scheme, avoiding unreasonable designs. There is a complex multivariate nonlinear relationship between the design parameters and performance indicators of the aircraft. The neural network proxy model and the multi-objective genetic algorithm can effectively handle this complex relationship to achieve the optimization design of the aircraft's multi-objective performance.

[0128] The aircraft overall optimization design method provided by the application, referring to Figure 11 The specific steps are as follows: S210, input parameters: the user inputs the initial parameters of the aircraft design through the parameter input and control sub-module of the human-computer interaction module, covering the shape parameters of the fuselage, wing and tail, engine performance parameters, avionics information and the like, and starts the system running.

[0129] S220, multidisciplinary calculation: The structure weight gravity estimation sub-module estimates the maximum aircraft weight and overall gravity according to the aircraft design parameters, avionics and engine performance parameters, and uses an empirical regression formula, and transmits the gravity data including the maximum aircraft weight and overall gravity to the aerodynamic calculation and analysis sub-module.

[0130] The aerodynamic calculation and analysis sub-module generates an OpenVSP script according to the input parameters, calls the OpenVSP program to calculate the aerodynamic parameters such as lift and drag, and completes the static stability margin analysis in combination with the gravity data.

[0131] The engine sub-module calculates the engine characteristic curves such as power curve and fuel consumption curve according to the input parameters such as engine type, power, cruising altitude and fuel consumption rate.

[0132] The performance calculation sub-module calculates the performance indicators of the aircraft such as take-off roll distance, cruising speed, range and 100 km fuel consumption by comprehensively calculating the data of the above three sub-modules.

[0133] S230, multi-objective optimization: the multi-objective genetic algorithm optimization sub-module takes the cruising speed, range and take-off roll distance as optimization objectives, and iteratively optimizes the initial population by the multi-objective genetic algorithm. In each generation evolution process, the better individuals are selected to form the next generation population by fast non-dominated sorting and congestion degree calculation, and the Pareto front solution set is updated constantly. If it does not converge, it continues to iterate. After multiple iterations, the algorithm converges to a set of non-dominated solutions, i.e. the Pareto optimal solution set.

[0134] S240, result output: the result display and output sub-module processes and displays the Pareto front solution set generated by the multi-objective genetic algorithm optimization sub-module, and intuitively presents the performance parameters of each design scheme in the form of charts and data lists, etc. for the user to compare and analyze. According to the user's demand, the detailed design report of the selected design scheme is output, including the geometric model, performance calculation process and results and the like information.

[0135] The application reduces manual intervention and design modification times through integrated control of the man-machine interaction module and multidisciplinary collaborative optimization, and the research and development cycle can be shortened by more than 50% compared with the traditional design method. The automatic script calling mechanism of the aerodynamic calculation and analysis module and the automatic characteristic curve generation function of the engine submodule further accelerate the data calculation speed and improve the overall design efficiency. The application realizes the coordinated optimization of multiple performance indicators such as aircraft cruising speed, range and take-off roll distance, breaks through the limitations of single-objective optimization through multi-objective genetic algorithm and multidisciplinary parameter collaborative adjustment, and improves the overall performance of the aircraft. Reducing design rework and test times, reducing manpower, material resources and time costs, and improving design resource utilization efficiency. The automatic calculation process reduces the error risk caused by manual operation and avoids repeated calculation and design modification caused by data errors. The Pareto front solution set generated by the multi-objective genetic algorithm provides multiple non-dominated design schemes, which can be compared in terms of aerodynamics, structure, fuel efficiency and other aspects to comprehensively weigh the advantages and disadvantages and make more scientific and reasonable design decisions.

[0136] Figure 12 An example of a schematic diagram of the physical structure of an electronic device is shown in Figure 12 The electronic device can include a processor 310, a communications interface 320, a memory 330 and a communications bus 340, wherein the processor 310, the communications interface 320 and the memory 330 communicate with each other through the communications bus 340. The processor 310 can call the logical instructions in the memory 330 to execute the aircraft overall optimization design method.

[0137] In addition, the logical instructions in the memory 330 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application or parts of the application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0138] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program being stored in a non-transitory computer readable storage medium, and the computer program, when executed by a processor, enables a computer to perform the aircraft overall optimization design method provided by the above methods.

[0139] In yet another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, enables the aircraft overall optimization design method provided by the above methods.

[0140] The apparatus embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed on a plurality of network units. Part or all of the sub-modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0141] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0142] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for overall aircraft optimization design, characterized in that, include: Obtain aircraft design parameters; The aircraft design parameters include wing and tail shape parameters, engine type, engine performance parameters, and cruising altitude; The weight of each component of the aircraft is calculated based on the aircraft design parameters, and the maximum takeoff weight and the center of gravity of the whole aircraft are calculated by combining the weight of each component with the center of gravity. Based on the aircraft design parameters and the overall center of gravity, each component is parametrically modeled, and aerodynamic calculations are performed to output lift and drag analysis results. An engine performance model is established based on the engine type and engine performance parameters, and a power curve and fuel consumption curve are generated based on the engine performance model. Based on the wing and tail shape parameters, the cruise altitude, the maximum takeoff weight, the lift and drag analysis results, the power curve, and the fuel consumption curve, multiple aircraft performance indicators are calculated. Using wing and tail shape parameters as design variables and multiple aircraft performance indicators as optimization objectives, multi-objective optimization is performed to generate a Pareto optimal solution set containing the optimal combination of wing and tail shape parameters.

2. The aircraft overall optimization design method according to claim 1, characterized in that, Based on the aircraft design parameters and the overall center of gravity, the various components are parametrically modeled, and aerodynamic calculations are performed to output lift and drag analysis results, including: The aircraft design parameters are parametrically modeled to generate parametric geometric definitions; A 3D model of the aircraft is generated based on the parametric geometric definition; The center of gravity of the entire aircraft is written into the three-dimensional model of the aircraft and set as the aerodynamic torque reference point; The calculation condition setting parameters are obtained, and based on the calculation condition setting parameters and the three-dimensional model of the aircraft, aerodynamic calculations are performed using the vortex lattice method to obtain the lift and drag analysis results.

3. The aircraft overall optimization design method according to claim 1, characterized in that, The method further includes: outputting static stability analysis results after performing aerodynamic calculations; the static stability analysis results include longitudinal static stability margin and lateral stability parameters. The longitudinal static stability margin is the difference between the focal position and the center of gravity position, and the lateral stability parameters are determined based on the derivatives of the heading static stability and the roll static stability.

4. The aircraft overall optimization design method according to claim 1, characterized in that, The power curve includes a power-speed curve and a power-altitude curve. The power-speed curve represents the mapping relationship between power and flight speed, and the power-altitude curve represents the mapping relationship between power and cruising altitude. The power is determined based on the power at sea level, flight speed, cruising altitude, and propeller efficiency.

5. The aircraft overall optimization design method according to claim 4, characterized in that, The aircraft performance indicators include cruise speed, range, and takeoff distance; the wing and tail shape parameters include wing area; and the lift-drag analysis results include lift coefficient and drag coefficient. Based on the wing and tail shape parameters, the cruise altitude, the maximum takeoff weight, the lift-drag analysis results, the power curve, and the fuel consumption curve, multiple aircraft performance indicators are calculated, including: The cruise efficiency factor is obtained, the engine output power corresponding to the cruise altitude is determined according to the power-altitude curve, and the cruise speed is determined according to the wing area, the cruise altitude, the maximum takeoff weight, the lift coefficient, the drag coefficient, the engine output power corresponding to the cruise altitude, and the cruise efficiency factor. The fuel consumption per unit time corresponding to the cruising altitude is determined based on the fuel consumption curve, and the range is determined based on the obtained fuel weight, the fuel consumption per unit time, and the cruising speed. Obtain the maximum lift coefficient, runway lift coefficient, runway drag coefficient, maximum engine power, runway height, ground friction drag coefficient, and wind speed. Determine the takeoff runway distance based on the wing area, maximum takeoff weight, maximum lift coefficient, runway lift coefficient, runway drag coefficient, maximum engine power, runway height, ground friction drag coefficient, and wind speed.

6. The aircraft overall optimization design method according to claim 1, characterized in that, The process involves multi-objective optimization, using wing and tail shape parameters as design variables and multiple aircraft performance indicators as optimization objectives, to generate a Pareto optimal solution set containing the optimal combinations of wing and tail shape parameters. This includes: The wing shape parameters and tail shape parameters are received as design variables, and an initial sample set is generated through Latin hypercube sampling. Multiple aircraft performance indicators are calculated as target values ​​based on each initial sample; The design variables and target values ​​of Latin hypercube sampling are paired as a training dataset. A pre-built neural network proxy model is trained based on the training dataset to establish the mapping relationship between design variables and target values, and the trained neural network proxy model is obtained. The training and evaluation process is repeated using a validation sample set to evaluate the model accuracy of the trained neural network proxy model until the model accuracy meets the preset requirements, thus obtaining the trained neural network proxy model. A multi-objective genetic algorithm is used to perform optimization iterations based on the trained neural network surrogate model, and output the Pareto optimal solution set.

7. An aircraft overall optimization design device, characterized in that, include: The parameter input and control submodule is used to acquire aircraft design parameters; The aircraft design parameters include wing and tail shape parameters, engine type, engine performance parameters, and cruising altitude; The structural weight and center of gravity estimation submodule is used to calculate the weight of each component of the aircraft based on the aircraft design parameters, and to calculate the maximum takeoff weight and the center of gravity of the entire aircraft by combining the weight of each component with the center of gravity. The aerodynamic calculation and analysis submodule is used to parametrically model each component based on the aircraft design parameters and the overall center of gravity, and to perform aerodynamic calculations to output lift and drag analysis results. The engine submodule is used to establish an engine performance model based on the engine type and engine performance parameters, and to generate power curves and fuel consumption curves based on the engine performance model. The performance calculation submodule is used to calculate multiple aircraft performance indicators based on the wing and tail shape parameters, the cruise altitude, the maximum takeoff weight, the lift and drag analysis results, the power curve, and the fuel consumption curve. The multi-objective genetic algorithm optimization submodule is used to perform multi-objective optimization with wing and tail shape parameters as design variables and multiple aircraft performance indicators as optimization objectives, generating a Pareto optimal solution set containing the optimal combination of wing and tail shape parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the overall aircraft optimization design method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the overall aircraft optimization design method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the overall aircraft optimization design method as described in any one of claims 1 to 6.

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