Airfoil optimization design method and system based on two-stage structure optimization strategy

Abstract

The present disclosure provides a wing profile optimization design method and system based on a two-stage structure optimization strategy, and relates to the technical field of aircraft wing design, which comprises obtaining geometric parameters of a wing profile to be optimized; based on the geometric parameters, coordinates of multiple control points of the upper and lower surfaces of the wing profile are calculated through a preset analytical model, a continuous wing profile contour is drawn based on the coordinates of the control points, and a wing profile sketch model is obtained; based on the wing profile sketch model, a wing profile optimization task is configured, a target function is constructed with the maximum lift-drag ratio as the optimization objective, variable ranges and constraint conditions are set, and the target function is solved by using a two-stage structure optimization strategy to obtain multi-objective wing profile design optimization parameters under complex constraints; a wing profile is generated based on the optimization parameters and used for aerodynamic simulation under different flight states, the input geometric parameters are dynamically adjusted based on the simulation results, and a geometric model is regenerated, thereby realizing closed-loop optimization iteration.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of aircraft wing design, in particular to a wing profile optimization design method and system based on a two-stage structure optimization strategy. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] The wing profile is a key configuration in the aerodynamic layout of an aircraft, directly affecting the core aerodynamic performance such as lift, drag, lift-drag ratio, and pressure center position. In modern wing profile optimization design, it usually includes parameterized modeling, numerical simulation analysis, and multi-objective optimization. Among them, parameterized modeling is the starting point of the optimization process, which determines the expression ability of the design space and the optimization effect.

[0004] In existing wing profile optimization design methods, platforms such as Python and MATLAB are often used for geometric parameter modeling, and third-party CFD simulation and optimization tools are called to complete performance analysis and parameter iteration. However, such methods generally have problems such as separation of modeling and simulation modules, untimely parameter updating, and lack of closed-loop control in the optimization process, especially when facing multi-condition, multi-stage task requirements, it is difficult to achieve dynamic adjustment of modeling accuracy and control range, affecting optimization efficiency and design quality. In addition, different flight stages correspond to different aerodynamic conditions, and traditional fixed parameterization methods are difficult to balance the adaptability of wing profile performance under multi-condition while maintaining geometric continuity, leading to unstable optimization convergence and design reproducibility engineering bottlenecks.

[0005] In addition, in existing optimization algorithms, a single optimization algorithm is often limited to one-sided advantages, some algorithms are good at global search, traversing the solution space through crossover and mutation, and some algorithms are good at local search and fast convergence. The calculation amount of a single optimization algorithm increases significantly when the population size is large or the number of selected generations is large, which is time-consuming and requires high computing resources. SUMMARY

[0006] To solve the above problems, the present disclosure proposes a wing profile optimization design method and system based on a two-stage structure optimization strategy, configures a wing profile optimization task, determines the optimization objective function, design variable range, and constraint conditions, adopts a genetic algorithm optimization strategy based on a two-stage structure, automatically adjusts the parameter granularity and optimization process according to different design stages, integrates with tools in a unified modeling environment, realizes rapid configuration, performance evaluation, and automatic optimization of the wing profile, and effectively improves the wing design efficiency and multi-condition adaptability.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] The airfoil optimization design method based on the two-stage structure optimization strategy comprises:

[0009] Obtaining geometric parameters of an airfoil to be optimized;

[0010] Based on the geometric parameters, coordinates of multiple control points of the upper and lower surfaces of the airfoil are calculated respectively by a preset analytical model, a continuous airfoil profile is generated based on the coordinates of the control points, and an airfoil sketch model is obtained;

[0011] Based on the airfoil sketch model, an airfoil optimization task is configured, a target function is constructed with the maximum lift-drag ratio as the optimization objective, variable ranges and constraint conditions are set, and the target function is solved by using a two-stage structure optimization strategy, thereby obtaining multi-objective airfoil design optimization parameters under complex constraints;

[0012] Based on the optimization parameters, an airfoil is generated and used for aerodynamic simulation under different flight states, the input geometric parameters are dynamically adjusted based on the simulation results, and a geometric model is regenerated, thereby realizing closed-loop optimization iteration; when the optimization meets the iteration conditions, the optimal parameters and the geometric model are output and exported in a standard CAD format;

[0013] The two-stage structure optimization strategy comprises a global search stage and a local development stage, in the local development stage, a particle swarm optimization algorithm is introduced for elite individuals with high fitness, the velocity and position are updated, and fine optimization of the local region is realized.

[0014] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0015] The airfoil optimization design system based on the two-stage structure optimization strategy comprises:

[0016] The parameter configuration module is configured to obtain geometric parameters of an airfoil to be optimized;

[0017] The geometric solving module is configured to calculate coordinates of multiple control points of the upper and lower surfaces of the airfoil respectively based on the geometric parameters by using a preset analytical model;

[0018] The curve construction module is configured to generate a continuous airfoil profile based on the coordinates of the control points, thereby obtaining an airfoil sketch model;

[0019] The model drawing module is configured to configure an airfoil optimization task based on the airfoil sketch model, construct a target function with the maximum lift-drag ratio as the optimization objective, set variable ranges and constraint conditions, and solve the target function by using a two-stage structure optimization strategy, thereby obtaining multi-objective airfoil design optimization parameters under complex constraints;

[0020] A parameter driving and updating module is configured to generate an airfoil based on optimization parameters, and to perform aerodynamic simulation under different flight states, dynamically adjust input geometric parameters based on simulation results, and regenerate a geometric model to realize closed-loop optimization iteration; when optimization meets iteration conditions, output optimal parameters and a geometric model, and export the same in a standard CAD format.

[0021] In the local development stage, the elite individuals with high fitness are introduced into a particle swarm optimization algorithm to update the speed and position and realize fine optimization in a local region.

[0022] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0023] A computer program product comprises a computer program, which, when executed by a processor, implements the airfoil optimization design method based on the two-stage structure optimization strategy.

[0024] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0025] A non-transitory computer-readable storage medium is configured to store computer instructions, which, when executed by a processor, implement the airfoil optimization design method based on the two-stage structure optimization strategy.

[0026] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0027] An electronic device comprises a processor, a memory and a computer program; the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the airfoil optimization design method based on the two-stage structure optimization strategy.

[0028] Compared with the prior art, the present disclosure has the following beneficial effects:

[0029] The airfoil optimization design method based on the two-stage structure optimization strategy realizes deep integration of parameter modeling, simulation analysis and multi-objective optimization in the same platform, and supports flexible parameter expression of airfoil geometry and adaptation to different design requirements.

[0030] The airfoil optimization design method based on the two-stage structure optimization strategy of the present disclosure constructs an objective function with the maximum lift-drag ratio as the optimization goal, sets the variable range and constraint conditions, and solves the objective function by using the optimization strategy of the two-stage structure. The two-stage genetic algorithm includes a global search stage and a local development stage. The global search stage is the first stage and is used for global rough search. The local development stage is the second stage and is used for local fine tuning. The airfoil optimization design method can automatically adjust the parameter granularity and the optimization strategy according to different design stages, can simultaneously optimize the performance indicators under multiple flight conditions, and improves the design adaptability. The algorithm introduces an adaptive adjustment mechanism supporting the crossover rate and the mutation rate, and uses the optimization algorithm combining the two stages. In the early stage, the genetic algorithm crossover and mutation are used to maintain the population diversity and avoid falling into local optimum too early. In the later stage, the particle swarm operation is used to further mine high-quality solutions, which makes it easier to find the global optimal airfoil scheme, adapts to the complex airfoil optimization demand, copes with the complex solution space such as multi-peak and high dimension, and improves the optimization success rate.

[0031] The airfoil optimization design method based on the two-stage structure optimization strategy of the present disclosure modularizes the tool structure, has good industrial deployability and secondary development potential, can be integrated with the tool in a unified modeling environment, combines performance simulation and optimization algorithm, realizes rapid configuration, performance evaluation and automatic optimization of the airfoil, and effectively improves the wing design efficiency and multi-condition adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0032] The drawings accompanying the specification of the present disclosure serve to provide further understanding of the present disclosure, and the illustrative embodiments of the present disclosure and the description thereof are used to explain the present disclosure, and do not constitute improper limitations on the present disclosure.

[0033] Figure 1 A process schematic diagram of the airfoil optimization design method based on the two-stage structure optimization strategy of the present disclosure embodiment;

[0034] Figure 2 A structure schematic diagram of the optimization strategy using the two-stage structure of the present disclosure embodiment;

[0035] Figure 3 A wing airfoil parameter input interface schematic diagram of the present disclosure embodiment;

[0036] Figure 4 A sketch model schematic diagram of the wing airfoil generated by the present disclosure embodiment;

[0037] Figure 5 A structure schematic diagram of the airfoil optimization design system and tool of the present disclosure embodiment. DETAILED DESCRIPTION

[0038] The present disclosure will be further described below in combination with the drawings and embodiments.

[0039] It should be noted that the following detailed description is illustrative only, and is intended to provide further description in order to provide a further understanding of the disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0040] It should be noted that the terms used herein are only intended to describe specific embodiments and are not intended to limit exemplary embodiments according to the disclosure. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, they indicate the presence of the features, steps, operations, devices, components and / or combinations thereof.

[0041] Embodiment 1

[0042] In an embodiment of the disclosure, a wing profile optimization design method based on a two-stage structure optimization strategy is provided, and the steps include:

[0043] Step 1: Obtain the geometric parameters of the wing profile to be optimized;

[0044] Step 2: Based on the geometric parameters, the coordinates of the control points on the upper and lower surfaces of the wing profile are calculated by a pre-set analytical model, and a continuous wing profile contour is generated based on the coordinates of the control points to obtain a wing profile sketch model;

[0045] Step 3: Based on the wing profile sketch model, configure the wing profile optimization task, construct the objective function with the maximum lift-drag ratio as the optimization objective, set the variable range and the constraint condition, and solve the objective function by using the optimization strategy of the two-stage structure to obtain the multi-objective wing profile design optimization parameters under complex constraints;

[0046] Step 4: Based on the optimization parameters, generate a wing profile and use it for aerodynamic simulation under different flight conditions, dynamically adjust the input geometric parameters based on the simulation results and regenerate the geometric model to realize closed-loop optimization iteration; when the optimization meets the iteration condition, output the optimal parameters and the geometric model, and export them in standard CAD format;

[0047] The optimization strategy of the two-stage structure includes a global search stage and a local development stage. In the local development stage, the elite individuals with high fitness are introduced based on the particle swarm optimization algorithm to update the speed and position and realize fine optimization in the local area.

[0048] As an embodiment, the wing profile optimization design method based on the two-stage structure optimization strategy of the disclosure provides a technical solution that can complete the closed loop of wing profile modeling, simulation and optimization in a unified platform, thereby improving the efficiency and quality of wing profile design. The specific implementation process is as follows:

[0049] Step 1: Obtain the geometric parameters of the airfoil to be optimized;

[0050] Specifically, key geometric parameters of the airfoil are input through the airfoil plugin interface in the CrownCAD platform. These include, but are not limited to, the leading edge radius, maximum thickness location, thickness curvature, and trailing edge wedge angle of the upper airfoil, and the leading edge direction, maximum outward cant location, outward cant slope, and trailing cant angle of the lower airfoil. The geometric parameters obtained in this step provide the input basis for subsequent geometric modeling.

[0051] Step 2: Based on the geometric parameters, calculate the coordinates of multiple control points on the upper and lower surfaces of the airfoil using a preset analytical model. Draw a continuous airfoil profile based on the coordinates of the control points to obtain the airfoil sketch model.

[0052] Specifically, based on the geometric parameters, the coordinates of seven control points on the upper and lower surfaces of the airfoil are calculated, and spline curves are automatically generated to form a continuous and smooth airfoil profile, resulting in an airfoil sketch model. This process is completed in the CrownCAD parts environment, and the sketch model can be updated in real time.

[0053] As one example, the point positions in the sketch model are calculated from the input geometric parameters, and the sketch reconstruction command is automatically executed to achieve a strong correlation between design parameters and geometric results. The sketch model supports subsequent modeling operations such as extrusion and Boolean operations, and is suitable for subsequent design stages such as airfoil 3D forming and structural integration. Its main features include that the upper and lower airfoil surfaces are each composed of spline curves; the leading and trailing edges are closed, meeting the requirements of closed curves; the centerline is set as a symmetry datum for subsequent structural layout; the sketch model has dimension annotation and parameter-driven association, supporting further extrusion to generate 3D geometry; the sketch data structure can be reused by other modules (such as structural design, aerodynamic analysis, etc.).

[0054] Specifically, a mathematical function model based on key airfoil parameters is used to calculate the coordinates of control points on the upper and lower airfoil surfaces, and the airfoil profile is generated using spline curves. The specific formulas and parameters are as follows:

[0055] The coordinates of the control points on the upper and lower wing surfaces are calculated using the following analytical expressions:

[0056] Upper wing surface expression:

[0057]

[0058] Lower wing surface expression:

[0059]

[0060] in, r le Where is the leading edge radius, tmax , x t are the maximum thickness value and position, respectively, o max , x o is the maximum dihedral angle and its position, k te , k te ` is the trailing edge wedge angle control parameter, a , b is the empirical coefficient for adjusting the curvature variation.

[0061] Further, the sketch model automatically applies constraint conditions in the drawing process to ensure that the upper and lower wings are smoothly connected at the leading and trailing edges, avoiding numerical non-convergence caused by geometric defects in simulation.

[0062] The constraint conditions include the coincidence constraint, which makes the upper wing leading edge endpoint, the lower wing leading edge endpoint, and the leading edge contour endpoint coincide, and the same for the trailing edge, ensuring that the upper and lower wings "meet" at the same point at the edge position.

[0063] Further, the coordinate calculation of the above seven control points uses a segmented control function to fit the leading edge, midsection, and trailing edge geometry of the airfoil, respectively, to ensure contour continuity and aerodynamic smoothness. The coordinate values of the control points can be displayed in the sketch and presented in the form of text annotations, facilitating user geometric feature checking and comparative analysis.

[0064] Specifically, the entire airfoil contour is divided into three sections in the chord direction, and different functions are used for modeling in different regions. Through the segmented function modeling method, the leading edge control is more flexible, the midsection controllability is enhanced, and the trailing edge adaptability is improved. The overall airfoil contour maintains geometric continuity and smoothness, meets the requirements of model stability and boundary processing for aerodynamic simulation in multiple working conditions, and realizes fine control of geometric shape, including:

[0065] The leading edge segment fitting function is used to control the smooth transition shape of the airfoil leading edge, which uses a square root function fitting as follows:

[0066]

[0067] where, x is the dimensionless coordinate along the chord direction, usually in the range [0, 1], representing the position from the leading edge (0) to the trailing edge (1), r le is the leading edge radius, x fe is the end point coordinate of the leading edge segment. This function can effectively ensure the continuity of the leading edge curvature.

[0068] The middle section fitting function is used to define the main thickness profile and shape change, and is expressed by a high-order polynomial as follows:

[0069]

[0070] wherein, a 0 a 4 is a fitting coefficient, x te is the coordinate of the end of the middle section.

[0071] The trailing edge section fitting function is used to construct the trailing edge wedge angle and closed profile, and is expressed by a power function as follows:

[0072]

[0073] wherein, m controls the trailing edge height, n controls the decay speed, adjusts the trailing edge contraction characteristics, and controls the stability of the trailing edge flow of the airfoil.

[0074] As an embodiment, as shown in FIG. 1, it is an airfoil parameter input interface schematic diagram. The interface is a plug-in front-end interface, including parameter input box, sliding bar, unit label and real-time verification feedback. Users can intuitively input each design parameter, and after modification, the system will trigger real-time modeling update. Figure 3

[0075] Specifically, the parameter interface is bound with the plug-in back-end modeling logic, supports parameter legality judgment, range limitation prompt, preview update and other functions, which helps to improve the modeling efficiency and design safety. Its main features include that the input fields are clearly classified as “upper surface parameters” and “lower surface parameters”; both sliding adjustment and numerical input modes are supported; real-time verification function is provided to highlight alarm when illegal values (such as negative thickness, angle out of limit, etc.) are input; parameter import and export functions are supported, which can save design templates and historical records; after modifying any parameter, the system automatically determines whether to rebuild the sketch, realizing instant feedback.

[0076] Further, the parameter input interface is connected with the geometry generation module through data binding technology, and users do not need to manually rebuild the sketch after each parameter modification. The system can automatically perform update operation in the background, ensuring the consistency of the geometric model and the parameters.

[0077] Further, the parameter input components in the interface include sliding adjustment bar, numerical input box, unit identification and synchronous display area, and can perform real-time verification and prompt for values exceeding the design boundary according to preset rules.

[0078] ​Step 3: configuring an airfoil optimization task based on the airfoil sketch model, constructing an objective function with the maximum lift-drag ratio as the optimization objective, setting the variable range and constraint conditions, and solving the objective function by using an optimization strategy with a two-stage structure to obtain the multi-objective airfoil design optimization parameters under complex constraints, including configuring the airfoil optimization task in the MATLAB environment, determining the optimization objective function (such as the maximum lift-drag ratio), the design variable range, the constraint conditions, and the optimization strategy, and the optimization strategy includes an improved genetic algorithm based on a two-stage structure.

[0079] Specifically, according to the design task, the optimization objective function is set, the maximum lift-drag ratio is selected as the optimization objective, the design variables and their upper and lower limits are selected, and the optimization algorithm is configured. The optimization strategy can be an improved two-stage genetic algorithm. The optimization strategy with a two-stage structure includes a global search stage and a local development stage. The global search stage is the first stage, which is used for global rough search. The local development stage is the second stage, which is used for local fine tuning.

[0080] The present disclosure takes the maximum lift-drag ratio as the optimization objective, and the objective function is designed as follows:

[0081]

[0082] wherein, C L is the lift coefficient; and C D is the drag coefficient. Both are calculated by a CFD analysis tool under specific attack angle, incoming flow velocity, and Reynolds number.

[0083] (1) Design variable range:

[0084] Leading edge radius 0.01-0.05;

[0085] Maximum thickness position 0.3-0.5;

[0086] Thickness curvature coefficient 5-20;

[0087] Trailing edge wedge angle coefficient 0-0.01;

[0088] (2) Constraint conditions:

[0089] The maximum thickness is not more than 20% of the chord;

[0090] The curvature of the upper and lower surfaces is continuous and does not produce fluctuations;

[0091] The aerodynamic performance meets C L >0.8, C D <0.05;

[0092] As an embodiment, the optimization strategy of the two-stage structure is as shown in Figure 2 The specific process is as follows:

[0093] The first stage is a global search stage, which adopts tournament selection, simulated binary crossover and multi-point mutation operators, performs n generations of iterations to maintain population diversity, and maintains the diversity and global coverage ability of the solution; the second stage is a local development stage, which introduces a speed and position update mechanism based on particle swarm optimization (PSO) for elite individuals with higher fitness, to realize fine optimization in the local region; at the same time, the algorithm also has an adaptive probability adjustment, and the crossover probability Pc and mutation probability Pm can be dynamically adjusted according to the fitness mean and population diversity index during the execution of the algorithm; when the population diversity (measured by Hamming distance or entropy value) is lower than the threshold θ, a catastrophe mechanism is triggered to reset part of the individuals to avoid falling into local optimum; the algorithm has the characteristics of fast convergence speed and strong ability to jump out of local optimum, and is suitable for multi-objective airfoil design problems under complex constraints.

[0094] Specifically, the detailed calculation steps of the global search and local development stages, the particle swarm mechanism, the adaptive adjustment strategy of the crossover and mutation probabilities, and the population catastrophe mechanism are provided as follows.

[0095] (1) Population initialization

[0096] An initial population containing N individuals is randomly generated, and each individual is an airfoil parameter combination vector (chromosome):

[0097]

[0098] Where d is the design variable dimension, such as leading edge radius, maximum thickness position and other parameters.

[0099] Initialize the particle swarm information: assign an initial speed Vi to each individual, and record its individual historical optimal position Pi, and the global optimal position g of the population.

[0100] (2) Iterative optimization

[0101] For the first n generations (rough global search stage):

[0102] Calculate the lift coefficient CL and drag coefficient CD of each individual airfoil using CFD to obtain the fitness function:

[0103]

[0104] Adjust the probability according to the current population fitness mean f, maximum value fmax and diversity index D:

[0105]

[0106] For the post-nth generation (fine local development stage):

[0107] Select the top E% of individuals by fitness as the elite subgroup, and perform particle swarm optimization (PSO) fine tuning.

[0108] The elite individuals are treated as "particles", and are updated according to the PSO rules:

[0109]

[0110] Where:

[0111] w is the inertia weight;

[0112] C1, C2 are learning factors;

[0113] r1, r2 are random numbers in [0, 1].

[0114] Then re-evaluate the fitness of the optimized individuals and update their personal best and global best positions. Merge the offspring generated by genetic operations with the elite of the particle swarm optimization to form a new population; continue to execute the main loop until the termination condition is met. Finally, output the airfoil parameters of the current optimal individual as the final design result.

[0115] As an embodiment, the following is the execution process of the improved two-stage genetic algorithm:

[0116] Improved genetic algorithm: two-stage structure + adaptive probability + catastrophe mechanism + particle swarm local optimization

[0117] Input: design variable range, optimization objective function f(x), population size N, maximum iteration G_max, stage division number n, fitness threshold ε

[0118] Output: optimal airfoil design parameters x_best

[0119] Initialize the population P ← {x_1, x_2,..., x_N}, and assign an initial speed to each individual v_i ←0

[0120] Record the best position of each individual p_i ← x_i, and initialize the global optimal solution g ← x_i (the individual with the best fitness)

[0121] For t = 1 to G_max do:

[0122] For each individual x_i in P:

[0123] Calculate the fitness f_i ← f(x_i)

[0124] Update individual best position p_i ← x_i if f(x_i)>f(p_i)

[0125] Update global best position g ← x_i if f(x_i)>f(g)

[0126] Compute population fitness mean f_avg, max f_max, diversity index D

[0127] Dynamically adjust crossover / mutation probabilities:

[0128] Pc ← Pc_max - (Pc_max - Pc_min) * (f_avg / f_max)

[0129] Pm ← Pm_min + (Pm_max - Pm_min) * (1 - D / D_max)

[0130] If t ≤ n:

[0131] First phase: global search (standard genetic algorithm)

[0132] Selection operation (roulette wheel / tournament)

[0133] Perform crossover operation (probability Pc)

[0134] Perform mutation operation (probability Pm)

[0135] If D< threshold θ:

[0136] Perform catastrophe: randomly replace some individuals

[0137] Else:

[0138] Second phase: local search (particle swarm optimization)

[0139] For elite individual x_e ∈ top E% elites:

[0140] Update velocity:

[0141] v_e ← ω * v_e + c1 * rand() * (p_e - x_e) + c2 * rand() * (g - x_e)

[0142] Update position:

[0143] x_e ← x_e + v_e

[0144] Re-evaluate fitness and update best positions

[0145] Update population P with new generation of individuals

[0146] If termination condition is met (f(g) > e or G_max is reached):

[0147] Break

[0148] Return g as the optimal solution x_best.

[0149] Step 4: Based on the optimization parameters, generate the airfoil and use it for aerodynamic simulation under different flight conditions, dynamically adjust the input geometric parameters based on the simulation results and regenerate the geometric model, realize closed-loop optimization iteration; when the optimization meets the iteration condition, output the optimal parameters and geometric model, and export it to standard CAD format;

[0150] Specifically, the generated airfoil is subjected to aerodynamic simulation under multiple flight conditions (including different Mach numbers, angles of attack and Reynolds numbers), considering multiple working condition combinations, including Mach number, angle of attack and Reynolds number condition, the simulation process is embedded in CrownCAD, or the lift coefficient, drag coefficient simulation data are obtained by calling XFOIL or Fluent tool through interface; based on the feedback of aerodynamic simulation results, dynamically adjust the airfoil geometric parameters, form a "parameter input → geometric modeling → performance simulation → parameter correction" closed-loop optimization cycle, and iteratively update the airfoil profile; when the optimization convergence condition is met, output the optimal airfoil parameters and the corresponding geometric model, support model export and data archiving.

[0151] As an embodiment, in the geometric parameter input process, all input parameters can be configured as optimization design variables, and the upper and lower limits and step length can be flexibly set in the optimization configuration interface.

[0152] In the optimization process, according to the aerodynamic feedback under different working conditions, it is automatically judged whether to switch the optimization stage, and the parameter control precision and variable sensitivity are adjusted accordingly, so as to realize the gradual optimization path from coarse adjustment to fine adjustment.

[0153] Embodiment 2

[0154] An airfoil optimization design system based on a two-stage structure optimization strategy is provided in an embodiment of the present disclosure, comprising:

[0155] A parameter configuration module is configured to obtain geometric parameters of an airfoil to be optimized;

[0156] A geometric solving module is configured to calculate coordinates of multiple control points on the upper and lower surfaces of the airfoil based on the geometric parameters through a preset analytical model;

[0157] A curve construction module is configured to generate a continuous airfoil profile based on the coordinates of the control points to obtain an airfoil sketch model.

[0158] a model drawing module configured to configure an airfoil optimization task based on an airfoil sketch model, construct an objective function with maximum lift-drag ratio as an optimization target, set a variable range and a constraint condition, and solve the objective function by using an optimization strategy of a two-stage structure to obtain multi-objective airfoil design optimization parameters under complex constraints;

[0159] a parameter driving and updating module configured to generate an airfoil based on the optimization parameters, perform aerodynamic simulation under different flight states, dynamically adjust input geometric parameters and regenerate a geometric model based on simulation results to realize closed-loop optimization iteration, and output optimal parameters and a geometric model in a standard CAD format when the optimization meets iteration conditions;

[0160] The optimization strategy of the two-stage structure includes a global search stage and a local development stage. In the local development stage, a particle swarm optimization algorithm is introduced for elite individuals with high fitness to update the speed and position and realize fine optimization in a local area.

[0161] As an embodiment, as shown in Figure 5 FIG. 1 is a structural schematic diagram of an airfoil optimization design system and tool. The system includes the following modules:

[0162] a parameter configuration module 11 configured to receive user input and configure airfoil design parameters;

[0163] a geometric solving module 12 configured to analyze the design parameters, call a point generation formula, and generate airfoil control points;

[0164] a curve construction module 13 configured to draw an airfoil profile in a sketch, control spline interpolation, leading and trailing edge closure, and reference line creation;

[0165] a model drawing module 14 configured to generate an airfoil geometric sketch and features and control sketch drawing and part generation;

[0166] a parameter driving and updating module 15 configured to trigger model updating after parameter modification, listen to parameter change events, and automatically execute sketch reconstruction;

[0167] As an embodiment, the method steps performed by the airfoil optimization design system based on the two-stage structure optimization strategy are as shown in Figure 1 FIG. 2, and are as follows:

[0168] S1: parameter input step, input airfoil key geometric parameters through an airfoil plug-in interface in a CrownCAD platform.

[0169] S2: Geometric construction step, according to the input parameters, the plug-in backend calculates the coordinates of the seven control points on the upper and lower surfaces of the airfoil, automatically generates a spline curve, and forms a continuous and smooth airfoil profile.

[0170] S3: Optimization configuration step, according to the design task, set the optimization objective function, such as maximizing the lift-drag ratio, select the design variables and their upper and lower limits, and configure the optimization algorithm.

[0171] S4: Multi-condition performance simulation step, the generated airfoil will be used for aerodynamic simulation under different flight states, considering various working condition combinations (such as Mach number, angle of attack, Reynolds number, etc.).

[0172] S5: Performance feedback and geometric adjustment step, the simulation results will be fed back to the optimization control module, and the optimizer will dynamically adjust the input parameters and regenerate the geometric model, realizing the "parameter input → geometric modeling → performance simulation → parameter correction" closed-loop optimization iteration.

[0173] S6: Optimization convergence and result output step, when the optimization meets the convergence condition or reaches the maximum iteration number, the system outputs the optimal parameters and the geometric model.

[0174] Specifically, the simulation optimization extension module can call external mainstream CFD tools such as XFOIL, Fluent or SU2, its interface design follows the open standard, and has good cross-platform adaptability.

[0175] Further, the data communication between the functional modules is carried out through the internal bus, which has the functions of module interface calling, parameter synchronization and execution state callback, and supports parallel computing and modular debugging under complex tasks.

[0176] Embodiment 3

[0177] In an embodiment of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the airfoil optimization design method based on the two-stage structure optimization strategy.

[0178] Embodiment 4

[0179] In an embodiment of the present disclosure, a non-transitory computer readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the airfoil optimization design method based on the two-stage structure optimization strategy.

[0180] Embodiment 5

[0181] An embodiment of the present disclosure provides an electronic device, comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the airfoil optimization design method based on the two-stage structure optimization strategy.

[0182] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 Figure 1 The functions specified in one flow or multiple flows and / or blocks.

[0183] These computer program instructions can also be loaded into a computer or other programmable data processing device to cause a series of operation steps to be executed on the computer or other programmable data processing device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing device provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 Figure 1 The functions specified in one flow or multiple flows and / or blocks.

[0184] The above description of specific embodiments of the present disclosure in conjunction with the accompanying drawings is not intended to limit the scope of protection of the present disclosure, and those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present disclosure without creative labor are still within the scope of protection of the present disclosure.​​​​​​​

Claims

1. An airfoil optimization design method based on a two-stage structural optimization strategy, characterized in that, include: Obtain the geometric parameters of the airfoil to be optimized; Based on the geometric parameters, the coordinates of multiple control points on the upper and lower surfaces of the airfoil are calculated using a preset analytical model. Based on the coordinates of the control points, a continuous airfoil profile is drawn to obtain the airfoil sketch model. Based on the airfoil sketch model, an airfoil optimization task is configured. An objective function is constructed with the maximum lift-to-drag ratio as the optimization objective. The range of variables and constraints are set. A two-stage optimization strategy is adopted to solve the objective function and obtain the multi-objective airfoil design optimization parameters under complex constraints. Airfoils are generated based on optimized parameters and used for aerodynamic simulation under different flight conditions. The input geometric parameters are dynamically adjusted and the geometric model is regenerated based on the simulation results to achieve closed-loop optimization iteration. When the optimization meets the iteration conditions, the optimal parameters and geometric model are output and exported as a standard CAD format. In this process, the airfoil optimization task is configured in the MATLAB environment, the optimization objective function is determined, the maximum lift-to-drag ratio is taken as the optimization objective, the design variables and their upper and lower limits are selected, and an improved two-stage genetic algorithm is used to solve the problem. The two-stage genetic algorithm includes a global search stage and a local development stage. The global search stage is the first stage, which is used for global coarse search, and the local development stage is the second stage, which is used for local fine-tuning. The global search phase employs standard genetic algorithms for selection, crossover, and mutation operations, performing n generations of iterations to maintain population diversity. In the local development phase, particle swarm optimization is introduced for highly fit elite individuals to update velocity and position, achieving refined optimization in local regions. Adaptive probability adjustment is incorporated into the algorithm, with the crossover probability P during execution. c With the probability of mutation P m The population is dynamically adjusted based on the mean fitness and population diversity index. When the population diversity is below the threshold θ, a catastrophe mechanism is triggered to reset some individuals to avoid getting trapped in local optima.

2. The airfoil optimization design method based on a two-stage structural optimization strategy as described in claim 1, characterized in that, By inputting key geometric parameters of the airfoil into the airfoil plugin interface in the CrownCAD platform, the geometric parameters include the leading edge radius, maximum thickness position, thickness curvature, and trailing edge wedge angle of the upper airfoil, and the leading edge direction, maximum outward cant position, outward cant slope, and trailing cant angle of the lower airfoil.

3. The airfoil optimization design method based on a two-stage structural optimization strategy as described in claim 1, characterized in that, Based on the geometric parameters, the coordinates of seven control points on the upper and lower surfaces of the airfoil are calculated using a piecewise control function. Spline curves are automatically generated and fitted to the leading edge, middle section, and trailing edge geometry of the airfoil to form a continuous and smooth airfoil profile, thus obtaining the airfoil sketch model. The calculation process is completed in the CrownCAD parts environment, and the airfoil sketch model can be updated in real time.

4. The airfoil optimization design method based on a two-stage structural optimization strategy as described in claim 1, characterized in that, Airfoils are generated based on optimized parameters for aerodynamic simulation under different flight conditions. Multiple combinations of operating conditions are considered, including Mach number, angle of attack and Reynolds number conditions. The simulation process is embedded in CrownCAD, or the lift coefficient and drag coefficient simulation data can be obtained by calling XFOIL or Fluent tools through the interface. Based on aerodynamic simulation data feedback, the airfoil geometric parameters are dynamically adjusted to form a parameter-performance closed-loop optimization cycle. The airfoil profile is iteratively updated. When the optimization convergence condition is met or the maximum number of iterations is reached, the optimal airfoil parameters and corresponding geometric model are output, supporting model export and data archiving.

5. An airfoil optimization design system based on a two-stage structural optimization strategy, specifically implementing the airfoil optimization design method based on a two-stage structural optimization strategy as described in any one of claims 1-4, characterized in that, include: The parameter configuration module is used to obtain the geometric parameters of the airfoil to be optimized; The geometry solving module is used to calculate the coordinates of multiple control points on the upper and lower surfaces of the airfoil based on the geometric parameters and using a preset analytical model. The curve construction module is used to draw and generate continuous airfoil profiles based on the coordinates of control points, thus obtaining an airfoil sketch model. The model drawing module is used to configure the airfoil optimization task based on the airfoil sketch model. It constructs an objective function with the maximum lift-to-drag ratio as the optimization objective, sets the variable range and constraints, and solves the objective function using a two-stage optimization strategy to obtain multi-objective airfoil design optimization parameters under complex constraints. The parameter-driven and update module is used to generate airfoils based on optimized parameters and to perform aerodynamic simulations under different flight conditions. It dynamically adjusts the input geometric parameters and regenerates the geometric model based on the simulation results to achieve closed-loop optimization iteration. When the optimization meets the iteration conditions, it outputs the optimal parameters and geometric model and exports them to standard CAD format. The two-stage optimization strategy includes a global search stage and a local development stage. In the local development stage, a particle swarm optimization algorithm is introduced for highly fit elite individuals to update their velocity and position, thereby achieving fine optimization in local areas.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the airfoil optimization design method based on the two-stage structural optimization strategy as described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the airfoil optimization design method based on a two-stage structural optimization strategy as described in any one of claims 1-4.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the airfoil optimization design method based on a two-stage structural optimization strategy as described in any one of claims 1-4.

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