A method, system, device, and storage medium for global sensitivity analysis of the aerodynamic characteristics of a damaged aircraft.

Abstract

This invention relates to the field of aeronautical engineering and aerodynamic characteristic analysis technology, specifically to a method, system, device, and storage medium for global sensitivity analysis of the aerodynamic characteristics of damaged aircraft. Targeting the damage characteristics of damaged aircraft, the method includes: acquiring samples of input parameters and output parameters; constructing a polynomial chaotic expansion model describing the mapping relationship between input and output parameters; generating a set of sampling points using a trajectory design method; calling the polynomial chaotic expansion model to obtain the output points corresponding to the sampling points; calculating the basic effects and the mean of the absolute values ​​of the basic effects; and ranking the input parameters using the mean of the absolute values ​​of the basic effects as a global sensitivity index to evaluate the degree of influence of each input parameter on the aerodynamic characteristics of the damaged aircraft. This invention uses a PCE model instead of wind tunnel tests or CFD simulations, and combines the low computational cost of the trajectory design method to reduce the number of calls to high-fidelity models, thereby improving the efficiency of sensitivity analysis of the aerodynamic characteristics of damaged aircraft.

Description

Technical Field

[0001] This invention relates to the field of aeronautical engineering and aerodynamic characteristic analysis technology, specifically to a method, system, device, and storage medium for global sensitivity analysis of the aerodynamic characteristics of a damaged aircraft. Background Technology

[0002] Damage to aircraft during operation can lead to structural damage such as skin perforation and wing surface defects, altering their original aerodynamic shape and affecting lift, drag, and moment characteristics, potentially jeopardizing flight safety in severe cases. Accurately assessing the aerodynamic characteristics of damaged aircraft and identifying key damage parameters is crucial for damage evaluation and flight safety assessment. For example, Chinese invention patent CN109697329A discloses an aerodynamic optimization calculation method for aircraft structural damage. Based on the vortex lattice method, it obtains aerodynamic data of aircraft subjected to varying degrees of structural damage to different parts of the wing and tail, and corrects the calculation results using wind tunnel test data of the aircraft without structural damage. This allows for rapid and accurate data acquisition at low cost and low risk, improving flight safety and the accuracy of aerodynamic modeling.

[0003] Currently, the main methods for obtaining the aerodynamic characteristics of damaged aircraft include wind tunnel testing and computational fluid dynamics (CFD) simulation. Although both can obtain relatively accurate results, they are both costly and time-consuming. In particular, when the coupled effects of multiple damage parameters need to be considered, the parameter combinations increase exponentially, and the computational load of full factor analysis is unbearable.

[0004] In evaluating the aerodynamic characteristics of damaged aircraft, global sensitivity analysis is an important method for assessing the influence of input parameters on the output response. Among existing methods, the Sobol method has high accuracy but requires tens of thousands of model evaluations, resulting in excessive computational costs; the Morris method achieves parameter selection with fewer evaluations through trajectory design, but it still requires direct access to a high-fidelity model, making its computational burden still heavy.

[0005] Therefore, there is an urgent need for a method that can efficiently and accurately identify key aerodynamic parameters of damaged aircraft. Summary of the Invention

[0006] To address the issues of high computational complexity in wind tunnel testing or CFD simulation of damaged aircraft startup characteristics in existing technologies, and the difficulty in balancing accuracy and efficiency with traditional sensitivity analysis methods, this invention provides a global sensitivity analysis method, system, device, and storage medium for the aerodynamic characteristics of damaged aircraft, enabling the identification of key aerodynamic parameters affecting damaged aircraft.

[0007] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for global sensitivity analysis of the aerodynamic characteristics of a damaged aircraft, comprising: Step 1: Based on the given input and output parameters, obtain samples of the input parameters and samples of the output parameters; The input parameters include damage size parameters, damage location parameters, and angle of attack, and the output parameters include lift coefficient. and rolling moment coefficient ; The input parameters are sampled based on a random representation model of the given input parameters; The output parameters are obtained through wind tunnel testing or CFD simulation. Step 2: Based on the samples of the input parameters and the samples of the output parameters, construct a multinomial chaotic expansion model that describes the mapping relationship between the input parameters and different output parameters. Step 3: Based on the stochastic representation model of the input parameters, a set of sampling points is generated using the trajectory design method, and the output points corresponding to the sampling points are obtained by calling the polynomial chaotic expansion model. Step 4: For each input parameter, calculate the basic effect based on the sampling points and the output points. and the mean of the absolute value of the basic effect ; Step 5, using the mean of the absolute values ​​of the basic effects The input parameters are ranked as global sensitivity indicators to evaluate the degree of influence of each input parameter on the aerodynamic characteristics of the damaged aircraft.

[0008] Preferably, in step 1, the damage size parameter includes at least one of the hole diameter, crack length, or skin loss area; the damage location parameter includes the relative position coordinates of the damage in the chord or spanwise direction of the wing.

[0009] Preferably, in step 2, the expression for the polynomial chaotic expansion model is:

[0010] in, For multidimensional orthogonal polynomial basis functions, For the input parameter vector, , k Indicates the number, Indicates the first k One input parameter; The coefficients to be determined are based on the input and output parameters and are determined using the least squares method or Bayesian sparse learning method. This represents the number of truncated terms.

[0011] Preferably, in step 3, the process of generating the sampling points is as follows: Several trajectories are generated within the space of the input parameters. Each trajectory contains multiple sampling points, and the starting point of each trajectory is randomly generated within the space of the input parameters. All trajectories cover the entire space of the input parameters. The number of sampling points contained in each trajectory is one more than the number of input parameters.

[0012] Preferably, during the generation of the sampling points, only one input parameter changes between adjacent sampling points.

[0013] Preferably, in step 4, for the first Input parameters The basic effect The calculation formula is:

[0014] in, This is the output of the polynomial chaotic expansion model. For the first The perturbation step size of each input parameter.

[0015] Preferably, the mean of the absolute values ​​of the basic effects The calculation formula is:

[0016] in, For the number of trajectories, For the first The first trajectory The basic effects of each input parameter; The larger the value, the greater the impact of the corresponding input parameter on the aerodynamic characteristics of the damaged aircraft.

[0017] Secondly, the present invention provides a system for implementing the aforementioned global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft, comprising: The data acquisition module is used to acquire samples of the input parameters and samples of the output parameters based on the given input parameters and output parameters. The model building module is used to construct, based on samples of the input parameters and samples of the output parameters, a multinomial chaotic expansion model describing the mapping relationship between the input parameters and different output parameters, respectively. The sampling point generation module is used to generate a set of sampling points based on the random representation model of the input parameters using the trajectory design method, and to call the polynomial chaotic expansion model to obtain the output points corresponding to the sampling points. The data processing module is used to calculate the basic effect of each input parameter based on the sampling points and the output points. and the mean of the absolute values ​​of the basic effects of each of the input parameters. ; The damage assessment module is used to calculate the mean of the absolute values ​​of the basic effects of each of the input parameters. The input parameters are ranked as global sensitivity indicators to evaluate the degree of influence of each input parameter on the aerodynamic characteristics of the damaged aircraft.

[0018] An electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method.

[0019] A storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0020] Compared with the prior art, the present invention has the following beneficial effects: This invention presents a global sensitivity analysis method for the aerodynamic characteristics of damaged aircraft. It employs a polynomial chaos expansion (PCE) model to replace wind tunnel testing or CFD simulation, combining this with the low computational cost of the trajectory design method to reduce the number of calls to high-fidelity models. The computational load is linearly related only to the number of input parameters, thus improving the efficiency of sensitivity analysis for damaged aircraft aerodynamic characteristics. Simultaneously, the PCE model approximates the nonlinear mapping relationship between input and output with high accuracy, achieving computational acceleration while maintaining accuracy, thereby avoiding the inaccuracies of traditional model methods. Furthermore, this invention, considering the characteristics of damaged aircraft, fully takes into account the influence of damage size and location parameters on the aerodynamic characteristics of the damaged aircraft, filling a technological gap in this field.

[0021] Furthermore, this invention quantifies and sorts the parameters by the mean of the absolute values ​​of the basic effects, thereby intuitively identifying key influencing parameters and providing a clear technical basis for damage assessment and flight safety judgment of damaged aircraft. Attached Figure Description

[0022] Figure 1 This is a flowchart of a global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to the present invention; Figure 2 This is a model diagram of the damaged aircraft in Example 1. Detailed Implementation

[0023] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0024] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0025] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0026] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0027] This invention discloses a global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft, referring to... Figure 1 ,include: Step 1: Based on the given input and output parameters, obtain samples of the input parameters and samples of the output parameters; The input parameters include damage size parameters, damage location parameters, and angle of attack. More specifically, the damage size parameters include at least one of the following: hole diameter, crack length, or area of ​​skin loss. The damage location parameters include the relative coordinates of the damage's position in the chord or spanwise direction of the wing. The output parameters include the lift coefficient. and rolling moment coefficient ; The input parameter samples are obtained by sampling from a random representation model based on the given input parameters; The output parameters are sampled through wind tunnel tests or CFD simulations.

[0028] Step 2: Based on samples of input parameters and output parameters, construct polynomial chaotic expansion models describing the mapping relationship between input parameters and different output parameters. In this invention, models describing the relationship between input parameters and lift coefficients are constructed respectively. The PCE model describing the mapping relationship between input parameters and rolling moment coefficients. The PCE model of the mapping relationship between them.

[0029] The PCE model will output the response. Represented as an input parameter vector A linear combination of orthogonal polynomial basis functions, expressed as:

[0030] in, For multidimensional orthogonal polynomial basis functions, For the input parameter vector, , k Indicates the number, Indicates the first k One input parameter; The coefficients to be determined are based on the input and output parameters and are determined using the least squares method or Bayesian sparse learning method. This represents the number of truncated terms.

[0031] Step 3: Based on the random representation model of the input parameters, a set of sampling points is generated using the trajectory design method, and the output points corresponding to the sampling points are obtained by calling the polynomial chaotic expansion model.

[0032] The stochastic representation model of the input parameters includes: assigning a preset probability distribution type and its value range to each input parameter. The probability distribution types include uniform distribution, normal distribution, log-normal distribution, Weibull distribution, etc.

[0033] Several trajectories are generated within the space of the input parameters. Each trajectory contains multiple sampling points, and the starting point of each trajectory is randomly generated within the space of the input parameters. All trajectories cover the entire space of the input parameters. The number of sampling points is one more than the number of input parameters.

[0034] The generation of sampling points follows the single perturbation principle, meaning that only one input parameter changes between adjacent sampling points.

[0035] Step 4: For each input parameter, calculate the basic effect based on the sampling points and output points. and the mean of the absolute value of the basic effect .

[0036] Among them, for the first Input parameters Basic effects The calculation formula is:

[0037] in, This is the output of the polynomial chaotic expansion model. For the first The perturbation step size of each input parameter. This represents the number of input parameters.

[0038] mean of the absolute value of the basic effect The calculation formula is:

[0039] in, The number of trajectories; For the first The first trajectory The basic effects of each input parameter; The larger the value, the greater the impact of the corresponding input parameter on the aerodynamic characteristics of the damaged aircraft.

[0040] Step 5, using the mean of the absolute values ​​of the basic effects The input parameters are ranked as global sensitivity indices to evaluate their impact on the aerodynamic characteristics of the damaged aircraft. The global sensitivity indices (i.e., the mean of the absolute values ​​of the basic effects) of each input parameter are used as the basis for evaluation. The sorting results are used to determine the degree of influence of each input parameter on the aerodynamic characteristics of the damaged aircraft. The larger the value, the more significant the impact of the input parameter on the aerodynamic characteristics of the damaged aircraft.

[0041] This invention presents a global sensitivity analysis method for the aerodynamic characteristics of damaged aircraft. It constructs a sensitivity analysis framework for the damage parameters of damaged aircraft, introducing a polynomial chaotic expansion (PCE) model to replace traditional wind tunnel tests or high-fidelity CFD simulations. Combined with the low sample size characteristic of track design, this reduces computational complexity from the exponential growth of traditional methods to a level linearly related to the number of input parameters. This reduces the number of high-fidelity model calls, making global sensitivity analysis of the complex nonlinear system of damaged aircraft possible even with limited computational resources, achieving an order-of-magnitude improvement in computational efficiency. Furthermore, the PCE model establishes a high-order nonlinear mapping relationship between the aerodynamic coefficients and damage parameters of the damaged aircraft based on orthogonal polynomial basis functions, allowing for direct analytical solutions based on the model coefficients, thus ensuring the engineering accuracy of the calculation results. In addition, this invention uses the mean of the absolute values ​​of basic effects to quantify and rank the input parameters, thereby intuitively and effectively assessing the impact of aircraft damage. This facilitates damage assessment and safety boundary determination, ultimately improving the intelligent protection of aircraft.

[0042] Example 1 Taking a certain type of fixed-wing aircraft as the research object, consider the typical damage to the aircraft's wing, such as... Figure 2 As shown. Three input parameters are given: damage size parameter, damage location parameter, and angle of attack. Where: Damage size parameters: based on hole diameter This indicates that the value range is 10 mm to 50 mm; Damage location parameter: the relative position of the damage center along the wing chord (chord position coefficient). This indicates that the value range is 0.2 to 0.8; Angle of attack The value range is -4° to 12°.

[0043] Given two output parameters: lift coefficient and rolling moment coefficient The above output parameters are key indicators for measuring aircraft aerodynamic characteristics and flight safety.

[0044] The stochastic representation model of each input parameter adopts a uniform distribution, and the specific distribution parameters are shown in Table 1.

[0045] Table 1 Distribution parameters of each input parameter in the stochastic representation model

[0046] Implementation steps: Step 1: Obtain samples of input parameters and output parameters using CFD simulation methods. Specifically: Within the range of three input parameters, 50 sets of input parameter samples were selected using the Latin hypercube sampling method. For each set of input parameter samples, a three-dimensional geometric model of the damaged aircraft was established, a structured mesh was generated, and CFD numerical simulation was performed to calculate the corresponding lift coefficient. and rolling moment coefficient This resulted in 50 input-output sample pairs, and some sample data are shown in Table 2.

[0047] Table 2 Partial Sample Data

[0048] Step 2: Based on the above 50 sets of input-output sample pairs, construct descriptions of the input parameters and lift coefficients respectively. The PCE model describing the mapping relationship between input parameters and rolling moment coefficients. The PCE model of the mapping relationship between them.

[0049] With lift coefficient Taking the PCE model as an example, it can be represented as an input parameter vector. Linear combination of Legendre orthogonal polynomial basis functions:

[0050] The coefficients of the PCE model are solved using the least squares method. The highest degree of the polynomial expansion is set to 3, and the number of truncated terms is determined. Using 50 sets of sample data, the coefficients were obtained through least squares regression. The estimated value.

[0051] The same method is used to construct the rolling moment coefficient. The PCE models were evaluated using cross-validation. The results showed that the relative prediction errors of both models were less than 5%, meeting the engineering accuracy requirements.

[0052] Step 3: Generate the sampling trajectory using the trajectory design method. Number of input parameters. Each trajectory contains One sampling point.

[0053] Set the number of tracks The specific generation process is as follows: For the Trajectory ( First, a starting point is randomly generated in the three-dimensional parameter space. Then, a fixed step size perturbation is applied to the 1st, 2nd, and 3rd parameters in sequence, with each perturbation changing only one parameter, and the subsequent 3 sampling points are generated in sequence.

[0054] 20 trajectories were generated. 80 sampling points. These 80 sets of input parameters are substituted into the PCE model constructed in step 2 to quickly calculate the corresponding lift coefficients. and rolling moment coefficient .

[0055] Step 4: Based on the 80 sampling points and their corresponding output points, calculate the basic effect of each input parameter.

[0056] With lift coefficient For example, for the first The trajectory, when the diameter of the disturbed hole When the basic effect is calculated, it is done by comparing two adjacent sampling points on the trajectory (only...). (The two points that changed) The difference between the output values ​​and The ratio of the changes in quantity.

[0057] For the diameter of the hole This parameter yielded a total of 20 basic effect values. Therefore, the diameter of the hole can be calculated. mean of the absolute value of the basic effect :

[0058] Similarly, the calculation yields... and .

[0059] For the rolling moment coefficient The mean of the absolute values ​​of the basic effects of each input parameter was calculated using the same method. The calculation results are shown in Table 3.

[0060] Table 3 Calculation results of global sensitivity index

[0061] Step 5: Based on the sorting results in Table 3, perform the following analysis: For lift coefficient Angle of attack of The maximum value indicates the angle of attack. It affects the lift coefficient The primary factor; chordal position coefficient Secondly; hole diameter The impact is minimal. This result is consistent with basic aerodynamic principles: angle of attack. Changes in the wing's surface directly alter the pressure distribution, having the most significant impact on lift; the location of the damage affects lift by altering the local aerodynamic shape; while the diameter of the hole has a relatively limited impact on overall lift within a certain range.

[0062] For the rolling moment coefficient chordal position coefficient of The maximum value indicates that the chordal location of the damage is a key factor affecting the rolling moment; the hole diameter Secondly; angle of attack The impact is minimal. This is because the rolling moment is mainly generated by the aerodynamic asymmetry of the left and right wings. When the damage is located at different chord positions, its impact on the offset of the wing pressure center is different, thus significantly changing the rolling moment characteristics.

[0063] The above embodiments use the hole diameter as the damage size parameter and the chordal position coefficient as the damage location parameter for illustration, but the scope of protection of this invention is not limited thereto. In other embodiments, the damage size parameter can also be the crack length or the area of ​​skin loss, and the damage location parameter can also be the relative position coordinates of the damage in the wing spanwise direction. In addition to the lift coefficient and roll moment coefficient, the output parameters may also include other aerodynamic characteristic parameters such as the drag coefficient, pitch moment coefficient, and lift-to-drag ratio.

[0064] Furthermore, besides the least squares method, the PCE model can also be constructed using Bayesian sparse learning; the stochastic representation model of the input parameters can be a normal distribution, log-normal distribution, or Weibull distribution, in addition to a uniform distribution. All of the above alternative implementation methods are within the scope of protection of this invention.

[0065] Based on the same concept, this invention also discloses a system for implementing a global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft, comprising: The data acquisition module is used to acquire samples of the input parameters and samples of the output parameters based on the given input parameters and output parameters. The model building module is used to construct a multinomial chaotic expansion model that describes the mapping relationship between the input parameters and different output parameters based on samples of input parameters and samples of output parameters. The sampling point generation module is used for a stochastic representation model based on input parameters. It generates a set of sampling points using the trajectory design method and calls the multinomial chaotic expansion model to obtain the output points corresponding to the sampling points. The data processing module is used to calculate the basic effect of each input parameter based on the sampling points and output points. and the mean of the absolute values ​​of the basic effects of each of the input parameters. ; The damage assessment module is used to calculate the mean of the absolute values ​​of the basic effects of each of the input parameters. The input parameters are ranked as global sensitivity indicators to evaluate the degree of influence of each input parameter on the aerodynamic characteristics of the damaged aircraft.

[0066] Based on the same concept, the present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from a computer storage medium to implement the corresponding method flow or corresponding function. Based on the same concept, this invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method. The computer-readable storage medium is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space containing the terminal's operating system. Furthermore, this storage space also contains one or more instructions suitable for loading and execution by a processor; these instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the technical solution of the present invention in any way. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can be modified and replaced in several simple ways, and these modifications and replacements are all within the scope of protection covered by the claims.

Claims

1. A method for global sensitivity analysis of the aerodynamic characteristics of a damaged aircraft, characterized in that, include: Step 1: Based on the given input and output parameters, obtain samples of the input parameters and samples of the output parameters; The input parameters include damage size parameters, damage location parameters, and angle of attack, and the output parameters include lift coefficient. and rolling moment coefficient ; The input parameters are sampled based on a random representation model of the given input parameters; The output parameters are obtained through wind tunnel testing or CFD simulation. Step 2: Based on the samples of the input parameters and the samples of the output parameters, construct a multinomial chaotic expansion model that describes the mapping relationship between the input parameters and different output parameters. Step 3: Based on the stochastic representation model of the input parameters, a set of sampling points is generated using the trajectory design method, and the output points corresponding to the sampling points are obtained by calling the polynomial chaotic expansion model. Step 4: For each input parameter, calculate the basic effect based on the sampling points and the output points. and the mean of the absolute value of the basic effect ; Step 5, using the mean of the absolute values ​​of the basic effects The parameters are ranked as global sensitivity indicators to evaluate the impact of each input parameter on the aerodynamic characteristics of the damaged aircraft.

2. The global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to claim 1, characterized in that, In step 1, the damage size parameter includes at least one of the hole diameter, crack length, or skin loss area; the damage location parameter includes the relative position coordinates of the damage in the chord or spanwise direction of the wing.

3. The global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to claim 1, characterized in that, In step 2, the expression for the polynomial chaotic expansion model is: in, For multidimensional orthogonal polynomial basis functions, For the input parameter vector, , k Indicates the number, Indicates the first k One input parameter; The coefficients to be determined are based on the input and output parameters and are determined using the least squares method or Bayesian sparse learning method. This represents the number of truncated terms.

4. The global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to claim 1, characterized in that, In step 3, the process of generating the sampling points is as follows: Several trajectories are generated within the space of the input parameters. Each trajectory contains multiple sampling points, and the starting point of each trajectory is randomly generated within the space of the input parameters. All trajectories cover the entire space of the input parameters. The number of sampling points contained in each trajectory is one more than the number of input parameters.

5. The global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to claim 4, characterized in that, During the generation of the sampling points, only one input parameter changes between adjacent sampling points.

6. The global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to claim 1, characterized in that, In step 4, for the first Input parameters The basic effect The calculation formula is: in, This is the output of the polynomial chaotic expansion model. For the first The perturbation step size of each input parameter.

7. The global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft according to claim 6, characterized in that, The mean of the absolute values ​​of the basic effects The calculation formula is: in, For the number of trajectories, For the first The first trajectory The basic effects of each input parameter; The larger the value, the greater the impact of the corresponding input parameter on the aerodynamic characteristics of the damaged aircraft.

8. A system for implementing the global sensitivity analysis method for the aerodynamic characteristics of a damaged aircraft as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire samples of the input parameters and samples of the output parameters based on the given input parameters and output parameters. The model building module is used to construct, based on samples of the input parameters and samples of the output parameters, a multinomial chaotic expansion model describing the mapping relationship between the input parameters and different output parameters, respectively. The sampling point generation module is used to generate a set of sampling points based on the random representation model of the input parameters using the trajectory design method, and to call the polynomial chaotic expansion model to obtain the output points corresponding to the sampling points. The data processing module is used to calculate the basic effect of each input parameter based on the sampling points and the output points. and the mean of the absolute values ​​of the basic effects of each of the input parameters. ; The damage assessment module is used to calculate the mean of the absolute values ​​of the basic effects of each of the input parameters. The parameters are ranked as global sensitivity indicators to evaluate the impact of each input parameter on the aerodynamic characteristics of the damaged aircraft.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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