A method and system for aerodynamic shape optimization design of special aircraft

Abstract

The present application relates to the technical field of aircraft aerodynamic design, in particular to a special aircraft aerodynamic shape optimization design method and system; the method comprises the following steps: building a special aircraft reference parameterized aerodynamic model, inputting aircraft flight and power reference parameters; selecting multiple groups of flight conditions to construct an aerodynamic-power coupling calculation model, calculating various resistances; determining multiple optimization objectives, constructing a multi-objective optimization function; configuring a multi-objective genetic algorithm to carry out iterative optimization; completing CFD simulation and power checking on the candidate shape; returning the simulation and checking data to the optimization algorithm for closed-loop iterative correction, and outputting the optimal aerodynamic shape after convergence; the system comprises the following modules: a reference parameterized modeling module, an aerodynamic-power coupling calculation module, a multi-objective optimization function construction module, a parameter iterative solution module, a candidate scheme simulation and checking module, and a closed-loop iterative optimization output module; the optimal special aircraft shape scheme that meets the requirements of aerodynamic performance and power matching can be obtained.

Description

Technical Field

[0001] This invention relates to the field of aircraft aerodynamic design technology, and in particular to a method and system for optimizing the aerodynamic shape of special aircraft. Background Technology

[0002] Special-purpose aircraft need to balance wide-speed-range flight and high-maneuverability flight capabilities. Their aerodynamic shape directly affects core aerodynamic performance characteristics such as lift-drag characteristics and flight stability. Currently, the aerodynamic shape design of special-purpose aircraft generally adopts a phased design approach, with aerodynamics and propulsion modules carried out separately. Typically, the aerodynamic shape design is completed first, and then the completed aerodynamic shape is handed over to the propulsion specialists for propulsion matching and verification. During the design process, designers often rely on manual comparison of schemes and parameter adjustments, selecting feasible shape schemes through multiple trial calculations.

[0003] However, in the traditional step-by-step design mode, the aerodynamic shape optimization process cannot be linked with the dynamic performance calculation in real time, making it difficult to obtain the optimal aircraft shape scheme that simultaneously meets the requirements of aerodynamic performance and dynamic matching. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing the aerodynamic shape of special aircraft, aiming to solve the technical problems (proposed in the prior art).

[0005] To achieve the above objectives, the present invention employs a special aircraft aerodynamic shape optimization design method, comprising the following steps: Build a baseline parameterized aerodynamic model for special aircraft, determine aerodynamic shape design parameters and limit the optimal value range of each parameter, and input the flight performance and power baseline parameters of the aircraft. Based on multiple preset flight conditions, an aerodynamic-dynamic coupling calculation model is established to solve the aerodynamic drag of pressure drag, friction drag, and induced drag. Combined with intake efficiency and flight velocity-pressure, the real-time engine thrust and fuel consumption are calculated. With high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and combined with constraints set by structural dimensions, flight load, and power limit parameters, a multi-objective optimization function is constructed. A multi-objective genetic algorithm is used to iteratively solve the core parameters of aerodynamic shape, and output multiple sets of candidate optimized shape parameter combinations for screening. CFD fluid simulation and dynamic performance matching verification were carried out on all candidate shapes. Data on aerodynamic coefficients, power output efficiency and fuel consumption under various working conditions were collected, and parameter combinations that did not meet the design threshold were eliminated. The performance data obtained from the simulation verification is fed back to the optimization algorithm to realize the closed-loop iterative correction of parameters. After the optimization results converge, the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters are output.

[0006] Among the steps involved in building a baseline parametric aerodynamic model for special-purpose aircraft, determining aerodynamic shape design parameters and limiting the optimal value range of each parameter, and simultaneously inputting the aircraft's flight performance and power baseline parameters: Obtain the original design scheme of the special aircraft, extract the basic geometric contour information of the fuselage, wings, tail and air intake, and establish an initial three-dimensional geometric reference model; The baseline model is parametrically processed, and the fuselage aspect ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle are converted into adjustable design variables. By combining the boundary conditions of the aircraft engineering, reasonable upper and lower limits are defined for each design variable, and the optimal value range for each parameter is obtained. Enter the aircraft's flight parameters and power baseline parameters to complete the model initialization configuration. The flight parameters include the design flight Mach number, flight altitude range, maximum maneuver overload, engine rated thrust, and fuel consumption baseline.

[0007] Among them, the steps of establishing an aerodynamic-dynamic coupling calculation model based on multiple preset flight conditions, solving for aerodynamic drag such as pressure drag, friction drag, and induced drag, and combining intake efficiency and flight velocity-pressure to complete the linkage calculation of engine thrust and fuel consumption in real time: Select multiple typical flight conditions covering low-speed takeoff, subsonic cruise, supersonic maneuvering, and extreme altitude, and read the basic atmospheric environmental data corresponding to each condition. The pressure drag, friction drag, and induced drag of the entire aircraft are calculated separately and then superimposed to obtain the overall aerodynamic drag of the aircraft under the corresponding operating conditions. Determine the working status of the air intake based on the flight status, calculate the air intake airflow capture efficiency, and evaluate the actual air intake capacity of the engine. By combining air intake capacity, flight conditions, and inherent engine characteristics, the real-time output thrust of the engine and the corresponding fuel consumption level are calculated, and the data correlation between aerodynamic performance and power performance is established.

[0008] Among them, in the step of constructing a multi-objective optimization function with high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and in combination with constraints set by structural dimensions, flight loads, and power limit parameters: The average lift-to-drag ratio across the entire flight envelope, the level of excess thrust, the overall fuel consumption level, and the maneuvering attitude stability of the aircraft were identified as the optimization targets. By analyzing the constraints of aircraft engineering, the maximum external dimensions, upper limit of structural load, upper limit of engine output, upper limit of fuel consumption, and high and low speed attitude stability requirements are extracted as constraint boundaries. Normalize each optimization objective to eliminate interference caused by the dimensional differences between different physical quantities; By integrating the optimization objective with all constraints, a multi-objective optimization function for the aerodynamic shape of special aircraft is formed.

[0009] In the step of iteratively solving the core parameters of aerodynamic shape using a multi-objective genetic algorithm and outputting multiple sets of candidate optimized shape parameter combinations for screening: Configure the parameters of the multi-objective genetic algorithm, setting the population size, total number of iterations, crossover probability, and mutation probability. An initial population is generated within the value range of each design parameter, and each individual represents a complete aerodynamic shape parameter scheme; Perform selection, crossover, and mutation genetic operations, combine optimization functions and constraints to evaluate the fitness of individuals in the population, and continuously update the population. The calculation stops after the preset number of iterations is reached, and multiple sets of candidate aerodynamic shape parameter combinations corresponding to the Pareto front are output.

[0010] Among them, the steps of conducting CFD fluid simulation and dynamic performance matching verification on all candidate shapes, collecting aerodynamic coefficients, power output efficiency, and fuel consumption data under various operating conditions, and eliminating parameter combinations that do not meet the design threshold are as follows: Each set of candidate parameters is transformed into a corresponding three-dimensional shape model of the aircraft, and the simulation preprocessing and computational domain meshing are completed. CFD fluid simulation calculations were performed for all typical flight conditions to collect aerodynamic performance data, including lift coefficient, drag coefficient, and stability coefficient. Input the aerodynamic results obtained from the simulation into the aerodynamic-power coupling calculation model to obtain power output efficiency and fuel consumption related performance data under various working conditions. By comparing various performance indicators with pre-set design thresholds, candidate parameter combinations that do not meet the design requirements are eliminated.

[0011] Among them, the steps of feeding back the performance data obtained from simulation verification to the optimization algorithm to achieve closed-loop iterative correction of parameters, and outputting the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters after the optimization results converge: The real performance data obtained through CFD simulation and dynamic verification is fed back to replace the performance results obtained by the approximate estimation within the original algorithm. The fitness evaluation is carried out again using the verified real performance data, which drives the algorithm to perform iterative optimization again and complete the closed-loop correction. The simulation verification and feedback iterative process is continuously repeated to compare the parameter changes in the outputs of the two rounds and determine whether the optimization results have reached the convergence condition. When the convergence criteria are met, the optimization process terminates, and the optimal aerodynamic shape parameters are output. At the same time, the dynamic parameters corresponding to each flight condition under this shape matching are also output.

[0012] This invention also provides a special aircraft aerodynamic shape optimization design system, including a baseline parameterized modeling module, an aerodynamic-dynamic coupling calculation module, a multi-objective optimization function construction module, a parameter iterative solution module, a candidate scheme simulation verification module, and a closed-loop iterative optimization output module; wherein: The benchmark parametric modeling module is used to build a benchmark parametric aerodynamic model of a special aircraft, determine the aerodynamic shape design parameters and limit the optimization value range of each parameter, and simultaneously input the flight indicators and power benchmark parameters of the aircraft. The aerodynamic-dynamic coupling calculation module is used to establish an aerodynamic-dynamic coupling calculation model based on multiple preset flight conditions, complete the aerodynamic drag solution of pressure drag, friction drag, and induced drag, and combine the intake efficiency and flight velocity-pressure to complete the linkage calculation of engine thrust and fuel consumption in real time. The multi-objective optimization function construction module is used to construct a multi-objective optimization function by taking high lift-to-drag ratio, appropriate power thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and combining structural dimensions, flight load, and power limit parameters to set constraints. The parameter iterative solution module is used to iteratively solve the core parameters of aerodynamic shape using a multi-objective genetic algorithm, and output multiple sets of candidate optimized shape parameter combinations for screening. The candidate scheme simulation verification module is used to perform CFD fluid simulation and power performance matching verification on all candidate shapes, collect aerodynamic coefficients, power output efficiency and fuel consumption data under various working conditions, and eliminate parameter combinations that do not meet the design threshold. The closed-loop iterative optimization output module is used to send the performance data obtained from simulation verification back to the optimization algorithm to realize closed-loop iterative correction of parameters. After the optimization results converge, it outputs the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters.

[0013] This invention discloses a method and system for optimizing the aerodynamic shape of a special-purpose aircraft. The method comprises the following steps: a reference parameterized aerodynamic modeling module, an aerodynamic-dynamic coupling calculation module, a multi-objective optimization function construction module, a parameter iterative solution module, a candidate scheme simulation verification module, and a closed-loop iterative optimization output module. These steps include: building a reference parameterized aerodynamic model of the special-purpose aircraft; determining aerodynamic shape design parameters and limiting the optimization value range of each parameter; and simultaneously inputting the aircraft's flight indicators and dynamic reference parameters. Based on multiple preset flight conditions, an aerodynamic-dynamic coupling calculation model is established to solve for pressure drag, friction drag, and induced drag. Combined with intake efficiency and flight velocity-pressure, the model performs real-time engine thrust and fuel consumption calculations. The system aims for a high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high efficiency. Dynamic stability is used as the optimization objective. Constraints are set based on structural dimensions, flight loads, and dynamic limit parameters to construct a multi-objective optimization function. A multi-objective genetic algorithm is used to iteratively solve the core parameters of the aerodynamic shape, outputting multiple sets of candidate optimized shape parameter combinations for screening. CFD fluid simulation and dynamic performance matching verification are performed on all candidate shapes, collecting aerodynamic coefficients, power output efficiency, and fuel consumption data under various operating conditions, and eliminating parameter combinations that do not meet the design thresholds. The performance data obtained from the simulation verification is fed back to the optimization algorithm to achieve closed-loop iterative correction of parameters. After the optimization results converge, the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters are output. Through the above methods, a closed-loop coupling of aerodynamic shape optimization and dynamic performance calculation is achieved, which can obtain the optimal special aircraft shape scheme that meets the requirements of aerodynamic performance and dynamic matching. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the steps in the aerodynamic shape optimization design method for special aircraft of the present invention.

[0016] Figure 2 This is a flowchart of steps S100 of the present invention.

[0017] Figure 3 This is a flowchart of steps S200 of the present invention.

[0018] Figure 4 This is a flowchart of steps S300 of the present invention.

[0019] Figure 5This is a flowchart of steps S400 of the present invention.

[0020] Figure 6 This is a flowchart of steps S500 of the present invention.

[0021] Figure 7 This is a flowchart of steps S600 of the present invention.

[0022] Figure 8 This is a schematic diagram of the principle of the special aircraft aerodynamic shape optimization design system of the present invention. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0024] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0026] Please see Figures 1-7 This invention provides a method for optimizing the aerodynamic shape of a special aircraft, comprising the following steps: S100: Build a baseline parameterized aerodynamic model for special aircraft, determine aerodynamic shape design parameters and limit the optimal value range of each parameter, and input the flight indicators and power baseline parameters of the aircraft.

[0027] In this embodiment, a baseline parameterized aerodynamic model of the special-purpose aircraft is built, the aerodynamic shape design parameters are determined, and the optimal value range of each parameter is limited. Simultaneously, the flight performance indicators and power baseline parameters of the aircraft are entered. The specific process is as follows: S101: Obtain the original design scheme of special aircraft, extract the basic geometric contour information of fuselage, wings, tail and air intake, and establish an initial three-dimensional geometric reference model; S102: Parametric processing of the baseline model, converting fuselage aspect ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle into adjustable design variables; S103: Combining the boundary conditions of the aircraft engineering, define reasonable upper and lower limits for each design variable to obtain the optimal value range for each parameter; S104: Input the aircraft's flight parameters and power reference parameters to complete the model initialization configuration. The flight parameters include the design flight Mach number, flight altitude range, maximum maneuver overload, engine rated thrust, and fuel consumption reference.

[0028] In the above process, the original two-dimensional drawings or preliminary three-dimensional design files of the special aircraft are read, and the coordinate point information of the fuselage axial contour line, wing planar contour and cross-sectional contour, tail contour, and air intake inlet and outlet and flow channel geometric contour is extracted respectively. The extracted discrete coordinate points of each component are imported into the three-dimensional modeling tool, and the contours of each component are reconstructed by surface fitting. The solid modeling of the fuselage, wings, tail and air intake is completed in sequence. Then, the components are assembled and the gaps are fused to eliminate the geometric breaks at the joints of the components, and a complete initial three-dimensional geometric reference model of the aircraft is generated.

[0029] Parametric analysis is performed on the generated initial 3D geometric reference model to remove the fixed geometric constraints of the model. The ratio of the total fuselage length to the maximum fuselage diameter is defined as the fuselage length-to-diameter ratio variable; the ratio of the wing span to the wing reference area is defined as the wing aspect ratio variable; the angle between the wing leading edge and the vertical fuselage axis is defined as the wing sweep angle variable; the ratio of the maximum airfoil thickness to the airfoil chord length is defined as the airfoil relative thickness variable; and the deflection angle of the horizontal stabilizer relative to the fuselage reference horizontal plane is defined as the horizontal stabilizer installation angle variable. The above geometric features are bound to the geometric driving relationship of the model. Modifying the variable values ​​can directly drive the synchronous update of the corresponding geometric shape of the 3D model, completing the data conversion process from fixed geometry to adjustable design variables.

[0030] Read the engineering boundary condition data such as the structural dimensions, structural strength, and assembly constraints of the aircraft, and conduct boundary checks on each design variable such as fuselage length-to-diameter ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle. Eliminate parameter values ​​that may cause structural interference, strength failure, or assembly failure. For each design variable, determine the maximum allowable value as the upper limit and the minimum allowable value as the lower limit, and bind and store each set of variable-upper limit-lower limit data to generate an optimized value range dataset for each parameter.

[0031] The system receives externally input parameters such as the design flight Mach number, flight altitude range, maximum maneuvering overload, engine rated thrust, and fuel consumption baseline. It performs format and rationality checks on the input parameters, eliminating abnormal input data that exceeds the boundaries of real-world engineering. The system then associates and binds the validated flight indicators and power baseline parameters with the aforementioned parameterized model and parameter optimization range, and writes the operating condition parameters and power parameters into the model configuration file, completing the full initialization configuration of the parameterized model.

[0032] S200: Based on multiple preset flight conditions, an aerodynamic-dynamic coupling calculation model is established to solve for aerodynamic drag such as pressure drag, friction drag, and induced drag. Combined with intake efficiency and flight velocity-pressure, the model completes the linkage calculation of engine thrust and fuel consumption in real time.

[0033] In this embodiment, an aerodynamic-dynamic coupling calculation model is established based on multiple preset flight conditions to solve for aerodynamic drag, including pressure drag, friction drag, and induced drag. Furthermore, by combining intake efficiency and flight velocity-pressure, the model performs real-time calculations of engine thrust and fuel consumption. The specific process is as follows: S201: Select multiple typical flight conditions covering low-speed takeoff, subsonic cruise, supersonic maneuvering, and extreme altitude, and read the basic atmospheric environmental data corresponding to each condition. S202: Calculate the pressure drag, friction drag, and induced drag of the whole machine respectively, and then sum them to obtain the aerodynamic drag of the aircraft under the corresponding operating conditions; S203: Determine the working status of the air intake based on the flight status, calculate the air intake airflow capture efficiency, and evaluate the actual air intake capacity of the engine. S204: Combining intake capacity, flight conditions, and inherent engine characteristics, solve for the real-time output thrust of the engine and the corresponding fuel consumption level, and establish a data correlation between aerodynamic performance and power performance.

[0034] In the above process, in accordance with the requirements for the use of the full envelope of the aircraft, four types of mission scenarios are selected: low-speed takeoff, subsonic cruise, supersonic maneuvering, and extreme altitude. For each type of scenario, corresponding flight speed, flight altitude, and flight attitude parameters are configured to form multiple sets of typical flight conditions. The standard atmospheric database is retrieved, and the corresponding atmospheric temperature, atmospheric pressure, air density, and other basic atmospheric environmental data are queried and matched according to the flight altitude index of each set of conditions. The condition identifier is mapped and stored one by one with the corresponding atmospheric environmental data, and the reading and grouping processing of the condition data is completed.

[0035] For a single flight condition, the pressure difference on the surfaces of the fuselage, wings, and tail is calculated based on the aerodynamic surface pressure distribution of the aircraft, and the overall pressure drag is obtained by integral solution. Laminar and turbulent regions are distinguished according to the flow state of the walls of each component, and the surface friction drag of each component is calculated by combining the wall area and atmospheric physical parameters, and the overall friction drag is obtained by summing them. The induced drag generated by the wing is calculated based on the lift distribution characteristics of the wing. The pressure drag, friction drag, and induced drag obtained under the same condition are numerically added together to complete the data fusion processing of multiple drag components, and the overall aerodynamic drag result of the aircraft under this condition is output.

[0036] The system reads the current flight Mach number and angle of attack parameters as flight status inputs, and determines whether the air intake is in a subcritical, critical, or supercritical operating state based on the air intake geometry. The air intake capture efficiency is obtained by comparing the captured airflow at the air intake with the theoretical inflow. Combined with atmospheric environmental parameters and capture efficiency, the actual airflow that can be supplied to the engine is calculated, thereby completing a quantitative assessment of the engine's actual air intake capability. The air intake operating status, capture efficiency, and actual airflow are then packaged and stored.

[0037] The engine's factory-calibrated inherent characteristic database is retrieved. This database contains mapping relationships between engine thrust and fuel consumption under different intake flow rates and flight conditions. The actual intake capacity data obtained in the previous step and the current flight condition parameters are input into the engine characteristic database for interpolation query processing to solve for the engine's real-time output thrust and corresponding fuel consumption level under this condition. The aerodynamic performance data such as overall aerodynamic drag calculated under the same condition are bound and paired with the thrust and fuel consumption power performance data according to the operating condition number and stored to construct a mapping data table of aerodynamic parameters and power parameters under the same operating condition, thus completing the data association between aerodynamic performance and power performance.

[0038] S300: The optimization objectives are high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high maneuverability. Constraints are set based on structural dimensions, flight loads, and power limit parameters to construct a multi-objective optimization function.

[0039] In this embodiment, high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high maneuverability are used as optimization objectives. Constraints are set based on structural dimensions, flight loads, and power limit parameters to construct a multi-objective optimization function. The specific process is as follows: S301: The average lift-to-drag ratio across the entire flight envelope, the level of excess thrust, the overall fuel consumption level, and the maneuvering attitude stability of the aircraft are identified as optimization targets. S302: Review the engineering constraints of the aircraft and extract the maximum external dimensions, upper limit of structural load, upper limit of engine output, upper limit of fuel consumption, and high and low speed attitude stability requirements as constraint boundaries; S303: Normalize each optimization objective to eliminate interference caused by the dimensional differences between different physical quantities; S304: Integrate the optimization objective with all constraints to form a multi-objective optimization function for the aerodynamic shape of special aircraft.

[0040] In the above process, all preset typical flight conditions are traversed, and the lift-to-drag ratio values ​​output by the simulation calculations for each condition are read. The average lift-to-drag ratio of all conditions is calculated to obtain the average lift-to-drag ratio of the entire flight envelope. The power thrust surplus level is obtained based on the difference between the aerodynamic drag of the entire aircraft and the real-time thrust of the engine for each condition. The fuel consumption data of the entire aircraft under each condition are statistically analyzed to obtain the overall fuel consumption level. The pitch and yaw stability coefficients of the aircraft under different maneuvering overload conditions are read to quantify the aircraft's maneuvering attitude stability index. The above four performance indicators are uniformly classified into a set of optimization targets, and the selection and definition of optimization targets are completed.

[0041] The system reads the overall design document, structural design document, and engine technical specification of the aircraft, and analyzes and extracts the engineering constraints within the documents. It selects the maximum external dimension to limit the geometric outline of the aircraft, extracts the upper limit of structural load to constrain the maximum load that the aircraft can withstand, reads the upper limit of engine output to limit the maximum output thrust of the engine, obtains the upper limit of fuel consumption to constrain the overall energy consumption level of the aircraft, and extracts the high and low speed attitude stability requirements to limit the allowable range of stability coefficients of the aircraft at different speeds. It then analyzes the upper and lower threshold limits corresponding to each constraint condition, stores the constraint type and threshold data in pairs, and generates a constraint boundary dataset.

[0042] The theoretical ranges for the average lift-to-drag ratio across the entire flight envelope, the power thrust margin, the overall fuel consumption, and the aircraft's maneuvering attitude stability are obtained, and the maximum and minimum values ​​for each optimization objective are determined. A linear normalization data processing method is used to map and transform the original physical quantities of each objective to a unified numerical range of 0-1. Objectives like lift-to-drag ratio, thrust margin, and attitude stability, where a higher expectation is better, undergo forward normalization, while objectives like fuel consumption, where a lower expectation is better, undergo reverse normalization. After normalization, the output results of all optimization objectives are within the same numerical range, eliminating computational interference caused by inconsistencies in physical units and magnitudes. The normalized mapping relationship and the normalized objective values ​​are then preserved.

[0043] The system reads the four normalized optimization objectives and imports all engineering constraint boundary datasets. It constructs a multi-objective optimization function framework, using the normalized optimization objectives as function outputs and setting the maximum external dimensions, structural load capacity, engine output capacity, fuel consumption capacity, and high / low speed attitude stability requirements as inequality constraints. It sets constraint over-limit judgment logic, applying a penalty term to any set of solutions when a parameter combination triggers an over-limit of any constraint boundary. The optimization objective calculation logic, constraint judgment logic, and penalty logic are encapsulated into a complete function calculation model, generating a multi-objective optimization function for the aerodynamic shape of special-purpose aircraft. This function receives aerodynamic shape design variables as input and outputs the evaluation results of each objective and the constraint satisfaction status.

[0044] S400: A multi-objective genetic algorithm is used to iteratively solve the core parameters of the aerodynamic shape, and output multiple sets of candidate optimized shape parameter combinations for screening.

[0045] In this embodiment, a multi-objective genetic algorithm is used to iteratively solve the core parameters of the aerodynamic shape, outputting multiple sets of candidate optimized shape parameter combinations for screening. The specific process is as follows: S401: Configure parameters for the multi-objective genetic algorithm, setting the population size, total number of iterations, crossover probability, and mutation probability; S402: Generate an initial population within the value range of each design parameter, with each individual representing a complete aerodynamic shape parameter scheme; S403: Perform selection, crossover, and mutation genetic operations, combine optimization functions and constraints to evaluate the fitness of individuals in the population, and continuously update the population; S404: Stop the operation after reaching the preset number of iterations, and output multiple sets of candidate aerodynamic shape parameter combinations corresponding to the Pareto front.

[0046] In the above process, the algorithm configuration input parameters are read to determine the population size (representing the total number of individuals in each generation), the total number of iterations (representing the maximum number of iterations for the algorithm to search for optimization), the crossover probability (representing the probability of gene exchange between individuals), and the mutation probability (representing the probability of random perturbation of parameters within an individual). These four parameters are written into the algorithm kernel configuration module of the multi-objective genetic algorithm to complete the parameter assignment process before the algorithm runs, lock the basic operating rules of the algorithm's optimization process, and verify the rationality of the parameters. Abnormal configurations that do not conform to engineering optimization experience, such as population size being too small or mutation probability being too high, are filtered out. The verified algorithm configuration parameters are then saved.

[0047] The optimal value ranges for each aerodynamic design variable obtained in step S103 are retrieved, with fuselage aspect ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle serving as individual gene loci. Within the upper and lower limit ranges corresponding to each variable, a set of parameters is generated by random sampling. This set of parameters constitutes an individual, corresponding to a complete set of aerodynamic shape parameter schemes for the aircraft. The corresponding number of individuals are repeatedly sampled according to the set population size, and all individuals together form the initial population dataset. Boundary checks are performed on each generated individual to ensure that all gene locus parameters fall within the corresponding value ranges. Out-of-bounds samples are removed, completing the initial population data generation process.

[0048] First, a selection operation is performed. Based on the distribution characteristics of each individual in the population, individuals are screened according to non-dominated sorting rules, retaining individuals with better performance as parent samples. Then, a crossover operation is performed, exchanging and recombinating the parameter gene loci of the parent individuals according to the crossover operation probability to generate a new generation of offspring individuals. Next, random perturbations within an interval are applied to some gene loci of the offspring individuals according to the mutation operation probability to complete the mutation process. All individuals obtained after selection-crossover-mutation are input into the multi-objective optimization function model constructed by S304, and fitness evaluation is carried out by substituting the constraint boundary conditions, calculating the objective performance score and constraint over-limit penalty value of each individual. Based on the fitness evaluation results, a new generation of effective population is selected, and individuals with poor fitness or constraint over-limit are eliminated, completing the population iterative update, and repeating the above genetic operation and fitness evaluation process.

[0049] The real-time statistical algorithm has completed a certain number of iterations. The current iteration number is compared with the preset total number of iterations. If the preset number of iterations has not been reached, the algorithm continues to return to the genetic iteration process of S403. When the number of iterations equals the preset total number of iterations, the algorithm terminates its internal iteration operation. Non-dominated solutions are screened for all individuals in the final generation population to extract non-dominated individuals on the Pareto front. Each non-dominated individual corresponds to a set of aerodynamic shape parameters. The multiple sets of parameters obtained are formatted, the parameter variable value information is stored, and multiple sets of candidate aerodynamic shape parameter combinations are output for subsequent simulation verification.

[0050] S500: Perform CFD fluid simulation and dynamic performance matching verification on all candidate shapes, collect aerodynamic coefficients, power output efficiency and fuel consumption data under various operating conditions, and eliminate parameter combinations that do not meet the design threshold.

[0051] In this embodiment, CFD fluid simulation and dynamic performance matching verification are performed on all candidate shapes. Data on aerodynamic coefficients, power output efficiency, and fuel consumption under various operating conditions are collected. Parameter combinations that do not meet the design thresholds are eliminated. The specific process is as follows: S501: Transform each set of candidate parameters into the corresponding three-dimensional shape model of the aircraft, and complete the simulation preprocessing and computational domain mesh generation. S502: Conduct CFD fluid simulation calculations for all typical flight conditions and collect aerodynamic performance data, including lift coefficient, drag coefficient, and stability coefficient. S503: Input the aerodynamic results obtained from the simulation into the aerodynamic-power coupling calculation model to obtain power output efficiency and fuel consumption related performance data under various operating conditions; S504: Compare each performance index with the pre-set design threshold and filter out any candidate parameter combinations that do not meet the design requirements.

[0052] In the above process, the single set of candidate aerodynamic shape parameters output by S404 are read, and the fuselage aspect ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle are input into the parametric modeling driver interface to drive the parametric model to update the geometric dimensions and generate a three-dimensional solid model of the aircraft corresponding to the set of parameters. The generated three-dimensional model is subjected to geometric inspection and processing to repair geometric defects such as broken surfaces, gaps, and intersecting interference. An external flow field computational domain surrounding the aircraft is constructed, the far-field boundary range is set, the mesh is refined in the aircraft wall area, and a gradient dilution mesh strategy is used in the far-field area to complete the surface mesh generation and volume mesh generation. Mesh quality inspection is carried out to remove mesh cells with excessive distortion, and a mesh file that can be used for fluid simulation is output. All candidate parameter combinations are processed iteratively to obtain the simulation mesh model corresponding to each scheme.

[0053] All typical flight conditions determined by S201 were retrieved, and the flight Mach number, angle of attack, and atmospheric environmental parameters corresponding to each condition were used as boundary conditions for CFD simulation. The mesh model output by S501 was imported into the fluid solver, the turbulence calculation model was set, and the flow field iterative calculation was started. The calculation was stopped after the flow field residuals converged and the aerodynamic results tended to stabilize. The simulation result file was post-processed and parsed, and the lift coefficient and drag coefficient of the aircraft were obtained by integral solution. The pitch stability coefficient was further calculated by torque calculation. The condition number was bound to the lift coefficient, drag coefficient, and stability coefficient under that condition and stored. The simulation calculation of all typical conditions was completed, and a complete set of aerodynamic performance datasets for the candidate shape was obtained.

[0054] Read the aerodynamic performance datasets for each operating condition output by S502, extract aerodynamic output results such as overall drag, and import the aerodynamic results along with the corresponding flight altitude, flight speed, and atmospheric parameters into the aerodynamic-dynamic coupling calculation model constructed by S200. The model internally reproduces the data processing logic of intake capture efficiency evaluation, intake capacity conversion, and engine characteristic interpolation query, and performs calculations and solutions in conjunction with the engine's inherent characteristic database. The engine power output efficiency, real-time thrust, and fuel consumption values ​​under each operating condition are calculated. The power output efficiency and fuel consumption data are associated with the operating condition number one by one to complete the conversion of aerodynamic simulation data to dynamic performance data, and output the full-condition dynamic performance dataset of the candidate shape.

[0055] The system reads a pre-stored set of design thresholds, which includes requirements for indicators such as the lower limit of lift-to-drag ratio, the lower limit of thrust margin, the upper limit of fuel consumption, and the allowable range of attitude stability coefficient. It retrieves the full-condition aerodynamic and dynamic performance data of the same candidate shape output from S502 and S503, and compares and verifies the measured performance indicators with the corresponding design thresholds for each condition. If any indicator of the candidate shape exceeds the allowable range of the threshold under any flight condition, the parameter combination is deemed unqualified. Unqualified candidate parameter combinations are marked and removed, and candidate schemes that meet all threshold requirements under all conditions are retained. This completes the screening process for candidate parameter combinations, and the filtered set of valid candidate parameters is output.

[0056] S600: The performance data obtained from simulation verification is fed back to the optimization algorithm to realize closed-loop iterative correction of parameters. After the optimization results converge, the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters are output.

[0057] In this embodiment, the performance data obtained from simulation verification is fed back to the optimization algorithm to achieve closed-loop iterative correction of parameters. After the optimization results converge, the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters are output. The specific process is as follows: S601: The real performance data obtained through CFD simulation and dynamic verification is fed back to replace the performance results obtained by the approximate estimation inside the original algorithm. S602: Use the verified real performance data to re-evaluate the fitness, drive the algorithm to perform iterative optimization again, and complete the closed-loop correction; S603: Continuous repeated simulation verification - feedback iteration process, compare the parameter change range of the output in the two rounds before and after, and determine whether the optimization result has reached the convergence condition; S604: When the convergence criteria are met, the optimization process is terminated, the optimal aerodynamic shape parameters are output, and the dynamic parameters corresponding to each flight condition under the shape matching are also output.

[0058] In the above process, the CFD simulation and dynamic verification data corresponding to the effective candidate schemes after S504 screening are read. The data includes indicators such as lift-to-drag ratio, thrust margin, fuel consumption, and maneuvering attitude stability under all operating conditions. The internal storage unit of the multi-objective genetic algorithm is located, and the corresponding performance results estimated by theoretical approximation formulas during the algorithm iteration are found. A data overlay and replacement method is adopted to overwrite the original approximate estimation values ​​with the real performance values ​​obtained from simulation verification. A mapping relationship between candidate parameter numbers and updated real performance data is established to complete the data backhaul update and eliminate the data deviation caused by the approximate estimation in the early stage of the algorithm.

[0059] The replaced real performance data is input into the multi-objective optimization function model constructed by S304. Normalization calculation and constraint condition judgment are re-executed for each individual in the population. The fitness score and constraint penalty value of each individual are recalculated. Based on the updated fitness evaluation results, the entire set of genetic operations of selection, crossover, and mutation are re-executed to generate a new generation of population individuals. The real simulation verification results are introduced into the algorithm optimization link to correct the optimization direction deviation caused by the original approximate calculation, complete the closed-loop correction process of the algorithm, and output the population after one round of closed-loop iteration update.

[0060] The updated new generation of individuals is fed back into the S501-S504 simulation verification loop, repeating the entire process of model generation, CFD simulation, dynamic verification, and scheme selection. The new round of simulation verification data is then fed back to the algorithm, and fitness re-evaluation and iterative optimization are carried out cyclically. After each closed-loop iteration, the design variable parameters of all individuals in the Pareto front of the current population are extracted and the difference is calculated with the corresponding individual parameters output from the previous closed-loop iteration. The average change of each design variable is then calculated. The average change of parameters is compared with a preset convergence threshold. If the average change of parameters is less than the preset convergence threshold, the result is considered convergent; if it is greater than the convergence threshold, it is considered non-convergent, and the next round of simulation verification-feedback iteration process continues.

[0061] After detecting that the convergence criteria are met, the simulation calculation and algorithm iteration loop is directly cut off, terminating the entire optimization process. In the Pareto front solution set after convergence, the optimal individual is selected from the non-dominated solutions according to the overall design preference. The fuselage aspect ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle corresponding to this individual are extracted as the optimal aerodynamic shape parameters. The full-condition simulation and dynamic verification database corresponding to this set of parameters is retrieved, and the engine output thrust, fuel consumption, and other dynamic condition parameters under each typical flight condition are extracted. The optimal aerodynamic shape parameters and the dynamic condition parameters of each flight condition are associated and encapsulated, and a standardized parameter set is output externally.

[0062] Please see Figure 8 This invention provides a special aircraft aerodynamic shape optimization design system, including a baseline parameterized modeling module, an aerodynamic-dynamic coupling calculation module, a multi-objective optimization function construction module, a parameter iterative solution module, a candidate scheme simulation verification module, and a closed-loop iterative optimization output module; wherein: The benchmark parametric modeling module is used to build a benchmark parametric aerodynamic model of a special aircraft, determine the aerodynamic shape design parameters and limit the optimization value range of each parameter, and simultaneously input the flight indicators and power benchmark parameters of the aircraft. The aerodynamic-dynamic coupling calculation module is used to establish an aerodynamic-dynamic coupling calculation model based on multiple preset flight conditions, complete the aerodynamic drag solution of pressure drag, friction drag, and induced drag, and combine the intake efficiency and flight velocity-pressure to complete the linkage calculation of engine thrust and fuel consumption in real time. The multi-objective optimization function construction module is used to construct a multi-objective optimization function by taking high lift-to-drag ratio, appropriate power thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and combining structural dimensions, flight load, and power limit parameters to set constraints. The parameter iterative solution module is used to iteratively solve the core parameters of aerodynamic shape using a multi-objective genetic algorithm, and output multiple sets of candidate optimized shape parameter combinations for screening. The candidate scheme simulation verification module is used to perform CFD fluid simulation and power performance matching verification on all candidate shapes, collect aerodynamic coefficients, power output efficiency and fuel consumption data under various working conditions, and eliminate parameter combinations that do not meet the design threshold. The closed-loop iterative optimization output module is used to send the performance data obtained from simulation verification back to the optimization algorithm to realize closed-loop iterative correction of parameters. After the optimization results converge, it outputs the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters.

[0063] In this embodiment, the reference parameterized modeling module builds a reference parameterized aerodynamic model of the special aircraft, determines the aerodynamic shape design parameters and limits the optimization value range of each parameter, and simultaneously inputs the aircraft's flight indicators and power reference parameters; the aerodynamic-power coupling calculation module establishes an aerodynamic-power coupling calculation model based on multiple preset flight conditions, completes the aerodynamic drag solution of pressure drag, friction drag, and induced drag, and combines intake efficiency and flight velocity-pressure to complete the linkage calculation of engine real-time thrust and fuel consumption; the multi-objective optimization function construction module uses high lift-to-drag ratio, appropriate power thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and sets constraints based on structural dimensions, flight load, and power limit parameters to construct a multi-objective optimization function; the parameter The iterative solution module employs a multi-objective genetic algorithm to iteratively solve the core parameters of the aerodynamic shape, outputting multiple sets of candidate optimized shape parameter combinations for screening. The candidate scheme simulation verification module performs CFD fluid simulation and dynamic performance matching verification on all candidate shapes, collecting aerodynamic coefficients, power output efficiency, and fuel consumption data under various operating conditions, and eliminating parameter combinations that do not meet the design threshold. The closed-loop iterative optimization output module feeds back the performance data obtained from the simulation verification to the optimization algorithm to achieve closed-loop iterative correction of parameters. After the optimization results converge, the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters are output. Through the above methods, a closed-loop coupling of aerodynamic shape optimization and dynamic performance calculation is achieved, enabling the acquisition of the optimal special aircraft shape scheme that meets the requirements of aerodynamic performance and dynamic matching.

[0064] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0065] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for optimizing the aerodynamic shape of a special-purpose aircraft, characterized in that, Includes the following steps: Build a baseline parameterized aerodynamic model for special aircraft, determine aerodynamic shape design parameters and limit the optimal value range of each parameter, and input the flight performance and power baseline parameters of the aircraft. Based on multiple preset flight conditions, an aerodynamic-dynamic coupling calculation model is established to solve the aerodynamic drag of pressure drag, friction drag, and induced drag. Combined with intake efficiency and flight velocity-pressure, the real-time engine thrust and fuel consumption are calculated. With high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and combined with constraints set by structural dimensions, flight load, and power limit parameters, a multi-objective optimization function is constructed. A multi-objective genetic algorithm is used to iteratively solve the core parameters of aerodynamic shape, and output multiple sets of candidate optimized shape parameter combinations for screening. CFD fluid simulation and dynamic performance matching verification were carried out on all candidate shapes. Data on aerodynamic coefficients, power output efficiency and fuel consumption under various working conditions were collected, and parameter combinations that did not meet the design threshold were eliminated. The performance data obtained from the simulation verification is fed back to the optimization algorithm to realize the closed-loop iterative correction of parameters. After the optimization results converge, the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters are output.

2. The aerodynamic shape optimization design method for special aircraft as described in claim 1, characterized in that, In the steps of building a baseline parameterized aerodynamic model for special-purpose aircraft, determining aerodynamic shape design parameters and limiting the optimal value range of each parameter, and simultaneously inputting the aircraft's flight performance and power baseline parameters: Obtain the original design scheme of the special aircraft, extract the basic geometric contour information of the fuselage, wings, tail and air intake, and establish an initial three-dimensional geometric reference model; The baseline model is parametrically processed, and the fuselage aspect ratio, wing aspect ratio, wing sweep angle, airfoil relative thickness, and horizontal stabilizer installation angle are converted into adjustable design variables. By combining the boundary conditions of the aircraft engineering, reasonable upper and lower limits are defined for each design variable, and the optimal value range for each parameter is obtained. Enter the aircraft's flight parameters and power baseline parameters to complete the model initialization configuration. The flight parameters include the design flight Mach number, flight altitude range, maximum maneuver overload, engine rated thrust, and fuel consumption baseline.

3. The aerodynamic shape optimization design method for special aircraft as described in claim 1, characterized in that, In the steps of establishing an aerodynamic-dynamic coupling calculation model based on multiple preset flight conditions, solving for aerodynamic drag such as pressure drag, friction drag, and induced drag, and combining intake efficiency and flight velocity-pressure to complete the linkage calculation of engine thrust and fuel consumption in real time: Select multiple typical flight conditions covering low-speed takeoff, subsonic cruise, supersonic maneuvering, and extreme altitude, and read the basic atmospheric environmental data corresponding to each condition. The pressure drag, friction drag, and induced drag of the entire aircraft are calculated separately and then superimposed to obtain the overall aerodynamic drag of the aircraft under the corresponding operating conditions. Determine the working status of the air intake based on the flight status, calculate the air intake airflow capture efficiency, and evaluate the actual air intake capacity of the engine. By combining air intake capacity, flight conditions, and inherent engine characteristics, the real-time output thrust of the engine and the corresponding fuel consumption level are calculated, and the data correlation between aerodynamic performance and power performance is established.

4. The aerodynamic shape optimization design method for special aircraft as described in claim 1, characterized in that, In the process of constructing a multi-objective optimization function with high lift-to-drag ratio across the entire flight envelope, appropriate thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and in combination with constraints set based on structural dimensions, flight loads, and power limit parameters: The average lift-to-drag ratio across the entire flight envelope, the level of excess thrust, the overall fuel consumption level, and the maneuvering attitude stability of the aircraft were identified as the optimization targets. By analyzing the constraints of aircraft engineering, the maximum external dimensions, upper limit of structural load, upper limit of engine output, upper limit of fuel consumption, and high and low speed attitude stability requirements are extracted as constraint boundaries. Normalize each optimization objective to eliminate interference caused by the dimensional differences between different physical quantities; By integrating the optimization objective with all constraints, a multi-objective optimization function for the aerodynamic shape of special aircraft is formed.

5. The aerodynamic shape optimization design method for special aircraft as described in claim 1, characterized in that, In the step of iteratively solving the core parameters of aerodynamic shape using a multi-objective genetic algorithm and outputting multiple sets of candidate optimal shape parameter combinations for screening: Configure the parameters of the multi-objective genetic algorithm, setting the population size, total number of iterations, crossover probability, and mutation probability. An initial population is generated within the value range of each design parameter, and each individual represents a complete aerodynamic shape parameter scheme; Perform selection, crossover, and mutation genetic operations, combine optimization functions and constraints to evaluate the fitness of individuals in the population, and continuously update the population. The calculation stops after the preset number of iterations is reached, and multiple sets of candidate aerodynamic shape parameter combinations corresponding to the Pareto front are output.

6. The aerodynamic shape optimization design method for special aircraft as described in claim 1, characterized in that, In the process of conducting CFD fluid simulation and dynamic performance matching verification on all candidate shapes, collecting aerodynamic coefficients, power output efficiency, and fuel consumption data under various operating conditions, and eliminating parameter combinations that do not meet the design threshold: Each set of candidate parameters is transformed into a corresponding three-dimensional shape model of the aircraft, and the simulation preprocessing and computational domain meshing are completed. CFD fluid simulation calculations were performed for all typical flight conditions to collect aerodynamic performance data, including lift coefficient, drag coefficient, and stability coefficient. Input the aerodynamic results obtained from the simulation into the aerodynamic-power coupling calculation model to obtain power output efficiency and fuel consumption related performance data under various working conditions. By comparing various performance indicators with pre-set design thresholds, candidate parameter combinations that do not meet the design requirements are eliminated.

7. The aerodynamic shape optimization design method for special aircraft as described in claim 1, characterized in that, In the process of feeding back the performance data obtained from simulation verification to the optimization algorithm to achieve closed-loop iterative correction of parameters, and outputting the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters after the optimization results converge: The real performance data obtained through CFD simulation and dynamic verification is fed back to replace the performance results obtained by the approximate estimation within the original algorithm. The fitness evaluation is carried out again using the verified real performance data, which drives the algorithm to perform iterative optimization again and complete the closed-loop correction. The simulation verification and feedback iterative process is continuously repeated to compare the parameter changes in the outputs of the two rounds and determine whether the optimization results have reached the convergence condition. When the convergence criteria are met, the optimization process terminates, and the optimal aerodynamic shape parameters are output. At the same time, the dynamic parameters corresponding to each flight condition under this shape matching are also output.

8. A special-purpose aircraft aerodynamic shape optimization design system, applied to the special-purpose aircraft aerodynamic shape optimization design method as described in claim 1, characterized in that, It includes a baseline parametric modeling module, an aerodynamic-dynamic coupling calculation module, a multi-objective optimization function construction module, a parameter iterative solution module, a candidate scheme simulation verification module, and a closed-loop iterative optimization output module; among which: The benchmark parametric modeling module is used to build a benchmark parametric aerodynamic model of a special aircraft, determine the aerodynamic shape design parameters and limit the optimization value range of each parameter, and simultaneously input the flight indicators and power benchmark parameters of the aircraft. The aerodynamic-dynamic coupling calculation module is used to establish an aerodynamic-dynamic coupling calculation model based on multiple preset flight conditions, complete the aerodynamic drag solution of pressure drag, friction drag, and induced drag, and combine the intake efficiency and flight velocity-pressure to complete the linkage calculation of engine thrust and fuel consumption in real time. The multi-objective optimization function construction module is used to construct a multi-objective optimization function by taking high lift-to-drag ratio, appropriate power thrust redundancy, low fuel consumption, and high maneuverability as optimization objectives, and combining structural dimensions, flight load, and power limit parameters to set constraints. The parameter iterative solution module is used to iteratively solve the core parameters of aerodynamic shape using a multi-objective genetic algorithm, and output multiple sets of candidate optimized shape parameter combinations for screening. The candidate scheme simulation verification module is used to perform CFD fluid simulation and power performance matching verification on all candidate shapes, collect aerodynamic coefficients, power output efficiency and fuel consumption data under various working conditions, and eliminate parameter combinations that do not meet the design threshold. The closed-loop iterative optimization output module is used to send the performance data obtained from simulation verification back to the optimization algorithm to realize closed-loop iterative correction of parameters. After the optimization results converge, it outputs the optimal aerodynamic shape of the special aircraft and the corresponding dynamic operating parameters.

Images