CN117763732A - Flexible wing structure load assessment method, electronic equipment, storage medium and device - Google Patents

Abstract

The invention discloses a flexible wing structure load assessment method, electronic equipment, storage media and a device. The method comprises the following steps: obtaining structural test data of the flexible wing, carrying out noise reduction treatment on the structural test data, establishing a flexible wing deformation model based on the structural test data subjected to the noise reduction treatment to obtain deformation data, establishing a flexible wing pneumatic load distribution model based on the deformation data and a regression model, combining the deformation model and the pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, calculating the structural load data of the flexible wing in the maneuvering process, evaluating the structural load data based on a clustering analysis method, and judging whether the calculation model is corrected according to an evaluation result until the evaluation result meets design requirements. According to the invention, the reliability and the accuracy of input data are improved through noise reduction, the abnormal working condition is accurately positioned through a clustering analysis method, the evaluation time is shortened, and the design efficiency of the flexible wing structure is improved.

Description

Flexible wing structure load assessment method, electronic equipment, storage medium and device

Technical Field

The invention belongs to the technical field of flexible wing aircraft design, and particularly relates to a flexible wing structural load assessment method, electronic equipment, a storage medium and a device.

Background

In recent years, the use of flexible airfoils has increased. However, flexible airfoils pose a number of challenges to solving structural loads. Firstly, for flexible wings, particularly wings with large aspect ratio, load distribution changes caused by wing deformation exist in the flight process, the changes not only include the influence of weight distribution and local overload caused by wing structural changes, but also the calculation difficulty of aerodynamic load parts caused by aerodynamic redistribution exists; secondly, the flexible wing amplifies the offset of the wing focus and the pressure center, and further amplifies the error value of the pneumatic load distribution condition, thereby causing the unbalance of the whole aircraft in the calculation process; meanwhile, the load of the flexible wing is highly correlated with the flight state of the aircraft, and wing load distribution forms with larger phase difference can be generated under different flight trends, so that the conversion processing capacity between the wing load and the flight attitude is greatly increased. Aiming at inputting a large number of complicated structural task sections, the possibility of omission and distortion exists in the structural strength based on the traditional load analysis method, and hidden risks are brought to structural design.

After the structural detailed design scheme of the flexible wing is defined, the flexible deformation modeling of the wing is needed to be solved firstly. In the conventional structural deformation analysis process, the characteristic parameters are intercepted by 'photographing' the structural vibration based on the structural test result, so that the modal confirmation of the wing structure is realized. Generally, a possible air and temperature environment is selected based on a design flight range in a preliminary design process, and a parameter point working condition with typical characteristics is simulated, and a large amount of data is required to be input in the confirmation process, however, in the engineering practice process, the related parameters obtained through the flexible wing aircraft structural test are greatly influenced by a test scene, and the temperature and humidity change of a test place, the non-interference-resistant condition of the test process, the measurement error in the actual test process, the additional interference of a sensor transmission channel and the like can have related influence on the reliability of a test result; in actual flight, there are complex deformation modes of the flexible wing. The structural deformations have different effects on the distribution of the aerodynamic loads, and even in case of excessive deformations the flight characteristics of the aircraft are affected by the redistribution of the aerodynamic loads. In general, for an aircraft, an excessive wing deformation condition should be avoided as much as possible to avoid possible flight safety risks, so that an excessive pneumatic load redistribution condition is not considered in the process of fitting the pneumatic load distribution, and therefore, an open loop loose coupling method is commonly used for calculating the pneumatic elastic characteristics of the flexible wing in engineering. However, the conventional method has a limitation in convergence speed, that is, the aerodynamic load distribution form and the change rate of the wing are required to maintain certain stability, and as the flexibility of the wing increases, the accuracy and the stability of the method are reduced.

The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

Disclosure of Invention

The invention aims to provide a flexible wing structural load assessment method, electronic equipment, a storage medium and a device, which are used for accurately assessing structural load distribution of a flexible wing in a maneuvering process, and meanwhile, the assessment time is shortened, the assessment accuracy is improved, and the structural design efficiency of the flexible wing is improved.

In order to achieve the above purpose, the invention provides a flexible wing structure load assessment method, electronic equipment, storage medium and device.

According to a first aspect of the present invention, there is provided a method of assessing the structural load of a flexible wing, comprising:

obtaining structural test data of the flexible wing, and carrying out noise reduction treatment on the structural test data;

establishing a flexible wing deformation model based on the structural test data after the noise reduction treatment is completed, and obtaining deformation data of the flexible wing;

establishing a flexible wing pneumatic load distribution model based on the deformation data and the regression model;

combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, and calculating structural load data of the flexible wing in a maneuvering process based on the calculation model;

and evaluating the structural load data based on a cluster analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement.

Optionally, the noise reduction processing is performed on the structural test data by a weighted filtering method, including:

selecting the structural test data through a window;

determining filtering parameters based on influencing factors of the structural test data in an actual structural test process;

and carrying out weighting processing on the structural test data based on the filtering parameters, and removing noise data of the structural test data.

Optionally, weighting the structural test data includes:

carrying out structuring treatment on the structural test data to obtain a structural test data distribution diagram;

determining coordinates of data points corresponding to each structural test data in the structural test data distribution diagram;

determining a theoretical center point of the structural test data based on the calculated data of the structural design stage;

weighting each data point based on the theoretical center point through a Gaussian distribution method;

and convolving each data point based on the filtering parameters, and determining and removing all noise data points in the data points according to convolution results.

Optionally, said convolving each of said data points comprises:

and accumulating weighted sums of all the data points in a convolution circle with the size of a convolution kernel as a diameter by taking the coordinate of each data point as a center, dividing the accumulated result by the weighted sum of the convolution circle, judging whether the calculated result is smaller than a set value, and if so, determining the data point as a noise data point.

Optionally, establishing the flexible wing aerodynamic load distribution model includes:

calculating wing aerodynamic load distribution data corresponding to different deformation states of the flexible wing in a maneuvering process based on an aerodynamic elasticity calculation example and the deformation data;

dispersing all the pneumatic load distribution data based on a finite element method to obtain wing pneumatic load distribution forms corresponding to typical deformation conditions;

integrating the discrete results and defining wing aerodynamic characteristics based on the local incoming flow angle of attack and sideslip angle;

acquiring regression characteristics of aerodynamic load dependent variables and the wing aerodynamic characteristics based on a polynomial regression model;

correcting the polynomial regression model based on a local overload factor and the flight dynamics;

and establishing the flexible wing pneumatic load distribution model based on the wing pneumatic load distribution form, the regression characteristic and the modified polynomial regression model.

Optionally, the local overload factor includes:

superposition of translational overload caused by gravity center linear acceleration difference and rotational overload caused by angular acceleration difference;

an aircraft center of gravity overload is calculated based on the wing aerodynamic load profile, and local overload factors at each finite element of the flexible wing are determined based on the aircraft center of gravity overload and the flight dynamics.

Optionally, the cluster analysis method comprises:

k means clustering algorithm;

and calculating key profile load data in the structural load data based on the K-means clustering algorithm, and further judging whether the structural load data amount in a clustering group in the clustering result is smaller than a set structural load data amount threshold value, if so, the maneuvering process and load distribution corresponding to the structural load data in the clustering group are problematic.

According to a second aspect of the present invention, there is provided a flexible wing structure load assessment apparatus comprising:

the acquisition and noise reduction processing module is used for acquiring structural test data of the flexible wing and carrying out noise reduction processing on the structural test data;

the first building module is used for building a flexible wing deformation model based on the structural test data after the noise reduction treatment is completed, and obtaining deformation data of the flexible wing;

the second building module is used for building a flexible wing pneumatic load distribution model based on the deformation data and the regression model;

the combination and calculation module is used for combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, and calculating structural load data of the flexible wing in the maneuvering process based on the calculation model;

and the evaluation and correction module is used for evaluating the structural load data based on a cluster analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement.

According to a third aspect of the present invention, there is provided an electronic device comprising:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the flexible wing structure load assessment method of any of the first aspects.

According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the flexible wing structure load assessment method of any of the first aspects.

The invention has the beneficial effects that: according to the invention, the noise reduction treatment is carried out on the structural test data, so that the influence of errors in the test process on the test result is reduced, and the reliability and accuracy of the input data are improved; establishing a flexible wing deformation model through the structure test data after the noise reduction treatment, and obtaining deformation data of the flexible wing with higher accuracy through the flexible wing deformation model; the flexible wing pneumatic load distribution model is established through the deformation data and the regression model, so that the calculation accuracy of the pneumatic load distribution model can be further improved; the flexible wing maneuvering process structure load distribution calculation model is obtained by combining the deformation model and the pneumatic load distribution model, so that the calculation of wing load distribution of the flexible wing in the maneuvering process is realized, and the accuracy of a calculation result can be improved; and based on the clustering analysis method, the structural load data is evaluated, whether the calculation model is corrected or not is judged according to the evaluation result, the abnormal working condition can be accurately positioned in Europe, the time for evaluation and analysis is shortened, the quality of the analysis process is improved, the evaluation accuracy is improved, and the design efficiency of the flexible wing structure is improved.

The system of the present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.

Drawings

The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.

FIG. 1 shows a flow chart of the steps of a flexible wing structure load assessment method according to the present invention.

Fig. 2 shows a flow chart of the steps of a flexible wing structure load assessment method according to embodiment 1 of the invention.

Fig. 3 shows a schematic view of a flexible wing structure load assessment device according to embodiment 1 of the invention.

Detailed Description

The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are illustrated in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

As shown in fig. 1, a method for evaluating a load of a flexible wing structure according to the present invention includes:

obtaining structural test data of the flexible wing, and carrying out noise reduction treatment on the structural test data;

establishing a flexible wing deformation model based on the structure test data after the noise reduction treatment to obtain deformation data of the flexible wing;

establishing a flexible wing pneumatic load distribution model based on the deformation data and the regression model;

combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, and calculating structural load data of the flexible wing in the maneuvering process based on the calculation model;

and evaluating the structural load data based on a cluster analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement.

Specifically, structural test data of the flexible wing are obtained from test results by carrying out structural test on the flexible wing, but the test results are often greatly influenced by test scenes, such as temperature and humidity change of a test place, non-anti-interference condition of the test process, measurement errors in the actual test process, additional interference of a sensor transmission channel and the like, can cause related influence on the reliability of the test results, and because the process of parameter selection and processing in the process is carried out based on a frequency domain, the test results can be subjected to noise reduction through weighted filtering, the influence of errors in the test process on the test results is reduced, the accuracy of the test data of the flexible wing deformation model structure is improved, and the accuracy of the deformation data of the flexible wing output by the flexible wing deformation model is further improved; then, a flexible wing pneumatic load distribution model is established based on deformation data and a regression model, namely, the flexible wing pneumatic load distribution model is established according to the deformation data of the flexible wing on the basis of the regression model, in the actual flight process, the flexible wing has a complex deformation mode, the deformation on the structure can generate different effects on the pneumatic load distribution mode, even under the condition of overlarge deformation, the flight characteristic of an aircraft can be influenced by the pneumatic load redistribution, in general, the overlarge wing deformation condition is avoided as much as possible for the aircraft so as to avoid possible flight safety risks, therefore, the pneumatic load distribution is fitted without considering the pneumatic load redistribution condition with overlarge amplitude, the flexible wing pneumatic elastic characteristic is calculated by a loose coupling method which is commonly used in engineering, however, the traditional method has the limitation on convergence speed, namely, the pneumatic load distribution mode and the change rate of the wing are required to keep certain stability, the accuracy and the stability of the result obtained by the traditional method can be reduced along with the increase of the flexibility of the wing, therefore, the invention adopts the mode of increasing the model complexity to solve the situation, namely, under the existing pneumatic load distribution is fitted under the condition of the pneumatic elastic support, and the pneumatic load distribution is based on the typical pneumatic deformation condition; then integrating discrete results, defining aerodynamic characteristics of the wing based on local incoming flow attack angles and sideslip angles, and providing conditions for data fitting; then, obtaining regression characteristics between dependent variables serving as pneumatic loads and independent variables such as local incoming flow attack angles, sideslip angles and the like through a regression model; in the process of discretizing pneumatic load, the pressure center obtained by calculation of the load distribution model always has a difference with the pressure center implied in the focus position or moment derivative used in flight dynamics, so that unbalance of the whole aircraft is caused, in order to control the influence of the error on the accuracy of the fitting model, the pneumatic load distribution model is evaluated by introducing a local overload factor, namely, the load distribution is calculated by taking a regression model as input, the aircraft center of gravity overload is calculated, and the local overload factor at each finite element unit of the flexible wing, namely, superposition of translational overload caused by gravity center linear acceleration difference and rotational overload caused by angular acceleration difference is determined by referring to flight dynamics results. When the local overload factor is too large, the regression model correction of the corresponding finite element is needed; after the regression model is stable, analyzing the positive wing based on the local overload factor, and processing the error of the pressing center in the aerodynamic load calculation process by using a method similar to inertial force, so that the error is eliminated as uniformly as possible in the whole machine range, the accuracy of the flexible wing aerodynamic load distribution model is improved, and the accurate calculation of the aerodynamic load distribution of each wing finite element by using the flexible wing aerodynamic load distribution model is realized; then combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structure load distribution calculation model, calculating structural load data of the flexible wing in the maneuvering process through the calculation model, evaluating a calculation result through a clustering analysis method, taking a K-means clustering algorithm as an example, randomly grouping key section load items processed by a large number of maneuvering processes through the calculation model, and determining an actual clustering center point of the flexible wing load through continuously iterating the clustering center and data grouping until data classification is stable, wherein wing load data in the clustering group with the data quantity lower than a preset value means that problems exist in the corresponding maneuvering processes and load distribution, the problems possibly exist in the flexible wing structure design, and the defects exist in the load distribution calculation model, and the calculation analysis amount can be greatly reduced through the evaluation of the clustering analysis method, and the risk of a design scheme can be reduced through processing data of a larger range under limited calculation resources by a relatively stable model; if the load distribution calculation model has defects, correcting the calculation model according to the evaluation result until the evaluation result meets the design requirement.

In one example, noise reduction processing is performed on structural test data through a weighted filtering method, including:

selecting structural test data through a window;

determining filtering parameters based on influencing factors of the structure test data in the actual structure test process;

and weighting the structural test data based on the filtering parameters to remove noise data of the structural test data.

Specifically, noise reduction processing is carried out on structural test data through a weighted filtering method, window data selection is carried out on the structural test data firstly, then filtering parameters are determined according to corresponding influence factors of the test data in the window in the actual structural test process, for example, the test environment temperature is the influence factor, and the filtering parameters are the test environment temperature; and finally, weighting the structural test data according to the filtering parameters to obtain noise data of the structural test data, thereby eliminating the noise data.

In one example, weighting structural test data includes:

carrying out structuring treatment on the structural test data to obtain a structural test data distribution diagram;

determining coordinates of data points corresponding to each structural test data in a structural test data distribution diagram;

determining a theoretical center point of structural test data based on the calculated data of the structural design stage;

weighting each data point based on a theoretical center point through a Gaussian distribution method;

and convolving each data point based on the filtering number, and determining and removing all noise data points in the data points according to the convolution result.

Firstly, carrying out structuring treatment on structural test data to obtain a structural test data distribution diagram, wherein each data point in the diagram represents one structural test data, then determining the coordinate of a data point corresponding to each structural test data in the structural test data distribution diagram, then determining a theoretical center point of the structural test data in the structural test data distribution diagram according to a calculated value in a structural design stage, acquiring the coordinate of the theoretical center point, calculating the shortest distance between each data point and the theoretical center point according to the coordinate of each data point and the coordinate of the theoretical center point, and weighting each data point by a Gaussian distribution method according to the shortest distance; after weighting is completed, each data point is convolved, that is, whether the data point is noise or not is judged according to the data quantity around the single data point.

In one example, convolving each data point includes:

and accumulating the weighted sum of all the data points in a convolution circle with the size of the convolution kernel as the diameter by taking the coordinate of each data point as the center, dividing the accumulated result by the weighted sum of the convolution circle, judging whether the calculated result is smaller than a set value, and if so, determining the data point as a noise data point.

Specifically, the coordinate of each data point is taken as the center, the size of the convolution kernel is taken as the diameter to form a convolution circle, the weighted sum of all the data points in the convolution circle is accumulated, the accumulated result is divided by the weighted sum of the convolution circle, whether the calculated result is smaller than a set value is judged, if yes, the data point is determined to be a noise data point, and the size of the convolution kernel is required to be adjusted according to the actual effect in order to better reject noise in the convolution process.

In one example, building a flexible airfoil aerodynamic load distribution model includes:

calculating wing aerodynamic load distribution data corresponding to different deformation states of the flexible wing in the maneuvering process based on the aerodynamic elasticity calculation example and the deformation data;

dispersing all pneumatic load distribution data based on a finite element method to obtain wing pneumatic load distribution forms corresponding to typical deformation conditions;

integrating the discrete results and defining wing aerodynamic characteristics based on the local incoming flow angle of attack and sideslip angle;

acquiring regression characteristics of aerodynamic load dependent variables and aerodynamic characteristics of the wing based on a polynomial regression model;

correcting the polynomial regression model based on the local overload factor and the flight dynamics;

and establishing a flexible wing pneumatic load distribution model based on the wing pneumatic load distribution form, the regression characteristic and the modified polynomial regression model.

In particular, in actual flight, there are complex deformation modes of the flexible wing. The structural deformations have different effects on the distribution of the aerodynamic loads, and even in case of excessive deformations the flight characteristics of the aircraft are affected by the redistribution of the aerodynamic loads. In general, for an aircraft, an excessive wing deformation condition should be avoided as much as possible to avoid possible flight safety risks, so that an excessive pneumatic load redistribution condition is not considered in the process of fitting the pneumatic load distribution, and therefore, an open loop loose coupling method is commonly used for calculating the pneumatic elastic characteristics of the flexible wing in engineering. However, the conventional method has a limitation in convergence speed, that is, the aerodynamic load distribution form and the change rate of the wing are required to maintain certain stability, and as the flexibility of the wing increases, the accuracy and the stability of the method are reduced. Thus, the under-fitting condition can be solved in a manner that increases the complexity of the model. Under the support of the existing aeroelasticity calculation example, the aerodynamic load distribution situation is discretized based on the finite element concept, and the aerodynamic distribution form of the wing under the typical deformation condition is obtained. And integrating discrete results, defining aerodynamic characteristics of the wing based on local incoming flow attack angles and sideslip angles, and providing conditions for data fitting. Then, a polynomial regression model is selected to obtain regression characteristics between dependent variables serving as pneumatic loads and independent variables such as local incoming flow attack angles, sideslip angles and the like, and pneumatic load calculation is carried out on each wing finite element by using the model. In the analysis of the aerodynamic load distribution of a flexible wing, there is often a local redistribution of aerodynamic loads. Therefore, in addition to the reference regression model, additional model calculations for a portion of the example interval need to be added in conjunction with the actual pneumatic analysis case.

In one example, the local overload factor includes:

superposition of translational overload caused by gravity center linear acceleration difference and rotational overload caused by angular acceleration difference;

the aircraft gravity center overload is calculated based on the wing aerodynamic load distribution form, and the local overload factors at each finite element of the flexible wing are determined based on the aircraft gravity center overload and flight dynamics.

Specifically, in the process of discretizing pneumatic load, the pressure center obtained by the load distribution calculation model always has a difference from the pressure center hidden in the focus position or moment derivative used in flight dynamics, so that unbalance of the whole aircraft is caused. To control the effect of this error on the accuracy of the fitted model, it is necessary to introduce a local overload factor to evaluate the aerodynamic load distribution model. Specifically, load distribution is calculated by using a regression model as input, the aircraft gravity center overload is calculated, and the local overload factors at each finite element unit of the flexible wing, namely the superposition of translational overload caused by gravity center linear acceleration difference and rotational overload caused by angular acceleration difference, are determined by referring to flight dynamics results. When the local overload factor is too large, regression model correction corresponding to the finite element is required. After the regression model is stable, the positive wing is analyzed based on the local overload factor, and the center-of-gravity errors in the aerodynamic force calculation process are processed by a method similar to the inertial force, so that the errors are eliminated as uniformly as possible in the whole machine range, and the accuracy of the regression model is improved.

In one example, the cluster analysis method includes:

k means clustering algorithm;

and calculating key profile load data in the structural load data based on a K-means clustering algorithm, and further judging whether the structural load data amount in a clustering group in a clustering result is smaller than a set structural load data amount threshold value, if so, solving the problem of maneuvering process and load distribution corresponding to the structural load data in the clustering group.

In particular, in practical engineering, the designed maneuver is intended to avoid abrupt load changes, so that in a reasonably continuous maneuver, when the time steps are taken for a sufficient time, the generated wing load data will not appear as a more obvious salient term, and based on this feature, the error point of the model can be locked. The invention adopts a K-means clustering algorithm to randomly group a large number of key section load items processed by a calculation model in the maneuvering process, and can determine the actual clustering center point of the flexible wing load by continuously iterating the clustering center and data grouping until the data classification is stable. The wing load data in the clustering group with the data volume lower than the preset value means that the corresponding maneuvering process and load distribution have problems. This problem may be a weakness in the design of the flexible wing structure, or a defect in the load distribution calculation model. In any case, the calculation model evaluation based on the cluster analysis can greatly reduce the calculation analysis amount, and can process the data in a larger range under the limited calculation resources through a more stable model, thereby reducing the risk of a design scheme.

The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.

Example 1

As shown in fig. 2, the present embodiment provides a method for evaluating a load of a flexible wing structure, including:

carrying out a structural mode test, obtaining structural test data of the flexible wing, and carrying out noise reduction treatment on the structural test data; carrying out noise reduction treatment on structural test data by a weighted filtering method, firstly carrying out window data selection on the structural test data, and then determining a filtering parameter according to corresponding influence factors of the test data in the window in the actual structural test process, wherein for example, the test environment temperature is the influence factor, and the filtering parameter is the test environment temperature; finally, carrying out weighting treatment on the structural test data according to the filtering parameters, namely firstly carrying out structuring treatment on the structural test data to obtain a structural test data distribution diagram, wherein each data point in the diagram represents one structural test data, then determining the coordinates of the data point corresponding to each structural test data in the structural test data distribution diagram, then determining the theoretical center point of the structural test data in the structural test data distribution diagram according to the calculated value of the structural design stage, obtaining the coordinates of the theoretical center point, calculating the shortest distance between each data point and the theoretical center point according to the coordinates of each data point and the coordinates of the theoretical center point, and weighting each data point according to the shortest distance by a Gaussian distribution method; after weighting is completed, each data point is convolved, namely whether the data point is noise or not is judged according to the data quantity around the single data point, the weighted sum of all the data points in the convolved circle is accumulated in the convolved circle with the size of the convolved kernel as the diameter by taking the coordinates of each data point as the center, the accumulated result is divided by the weighted sum of the convolved circle, whether the calculated result is smaller than a set value is judged, if yes, the data point is determined to be the noise data point, the noise data of the structural test data is obtained, and the noise data is further eliminated;

establishing a flexible wing deformation model based on the structure test data after the noise reduction treatment to obtain deformation data of the flexible wing;

establishing a flexible wing pneumatic load distribution model based on the deformation data and the regression model; in actual flight, there are complex deformation modes of the flexible wing. The structural deformations have different effects on the distribution of the aerodynamic loads, and even in case of excessive deformations the flight characteristics of the aircraft are affected by the redistribution of the aerodynamic loads. In general, for an aircraft, an excessive wing deformation condition should be avoided as much as possible to avoid possible flight safety risks, so that an excessive pneumatic load redistribution condition is not considered in the process of fitting the pneumatic load distribution, and therefore, an open loop loose coupling method is commonly used for calculating the pneumatic elastic characteristics of the flexible wing in engineering. However, the conventional method has a limitation in convergence speed, that is, the aerodynamic load distribution form and the change rate of the wing are required to maintain certain stability, and as the flexibility of the wing increases, the accuracy and the stability of the method are reduced. Thus, the under-fitting condition can be solved in a manner that increases the complexity of the model. Under the support of the existing aeroelasticity calculation example, the aerodynamic load distribution situation is discretized based on the finite element concept, and the aerodynamic distribution form of the wing under the typical deformation condition is obtained. And integrating discrete results, defining aerodynamic characteristics of the wing based on local incoming flow attack angles and sideslip angles, and providing conditions for data fitting. Then, a polynomial regression model is selected to obtain regression characteristics between dependent variables serving as pneumatic loads and independent variables such as local incoming flow attack angles, sideslip angles and the like, and pneumatic load calculation is carried out on each wing finite element by using the model. In the analysis of the aerodynamic load distribution of a flexible wing, there is often a local redistribution of aerodynamic loads. Therefore, in addition to the reference regression model, additional model calculations for a portion of the example interval need to be added in conjunction with the actual pneumatic analysis case. In the process of discretizing pneumatic load, the pressure center obtained by the load distribution calculation model always has a difference from the pressure center implied in the focus position or moment derivative used in flight dynamics, so that the imbalance of the whole aircraft is caused. To control the effect of this error on the accuracy of the fitted model, it is necessary to introduce a local overload factor to evaluate the aerodynamic load distribution model. Specifically, load distribution is calculated by using a regression model as input, the aircraft gravity center overload is calculated, and the local overload factors at each finite element unit of the flexible wing, namely the superposition of translational overload caused by gravity center linear acceleration difference and rotational overload caused by angular acceleration difference, are determined by referring to flight dynamics results. When the local overload factor is too large, regression model correction corresponding to the finite element is required. After the regression model is stable, the positive wing is analyzed based on the local overload factor, and the center-of-gravity errors in the aerodynamic force calculation process are processed by a method similar to the inertial force, so that the errors are eliminated as uniformly as possible in the whole machine range, and the accuracy of the regression model is improved.

Combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, and calculating structural load data of the flexible wing in the maneuvering process based on the calculation model;

and evaluating the structural load data based on a cluster analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement. In practical engineering, the designed maneuver is expected to avoid abrupt load mutation, so that the generated wing load data will not appear obvious salient items when the time steps are taken for a small enough time in a reasonable continuous maneuver, and the model error point can be locked based on the characteristic. According to the embodiment, a K-means clustering algorithm is adopted, key section load items processed by a calculation model in a large number of maneuvering processes are randomly grouped, and an actual clustering center point of the flexible wing load can be determined by continuously iterating the clustering center and data grouping until data classification is stable; the wing load data in the clustering group with the data volume lower than the preset value means that the corresponding maneuvering process and load distribution have problems. This problem may be a weakness in the design of the flexible wing structure, or a defect in the load distribution calculation model. In any case, the calculation model evaluation based on cluster analysis can greatly reduce the calculation analysis amount, and can process larger-range data under limited calculation resources through a more stable model, so that the risk of a design scheme is reduced; if the load distribution calculation model has defects, correcting the calculation model according to the evaluation result until the evaluation result meets the design requirement.

Example 2

As shown in fig. 3, the present embodiment provides a flexible wing structure load assessment device, including:

the acquisition and noise reduction processing module is used for acquiring structural test data of the flexible wing and carrying out noise reduction processing on the structural test data;

the first building module is used for building a flexible wing deformation model based on the structure test data after the noise reduction treatment is completed, so as to obtain deformation data of the flexible wing;

the second building module is used for building a flexible wing pneumatic load distribution model based on the deformation data and the regression model;

the combination and calculation module is used for combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structure load distribution calculation model, and calculating structure load data of the flexible wing in the maneuvering process based on the calculation model;

and the evaluation and correction module is used for evaluating the structural load data based on the clustering analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement.

Example 3

The present embodiment provides an electronic device including:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the flexible wing structure load assessment method of embodiment 1.

An electronic device according to an embodiment of the present disclosure includes a memory for storing non-transitory computer-readable instructions and a processor. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.

The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.

It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.

The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.

Example 4

The present embodiment provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the flexible wing structure load assessment method in embodiment 1.

A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the present disclosure described above.

The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.

Claims (10)

1. A method of flexible wing structural load assessment, comprising:

obtaining structural test data of the flexible wing, and carrying out noise reduction treatment on the structural test data;

establishing a flexible wing deformation model based on the structural test data after the noise reduction treatment is completed, and obtaining deformation data of the flexible wing;

establishing a flexible wing pneumatic load distribution model based on the deformation data and the regression model;

combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, and calculating structural load data of the flexible wing in a maneuvering process based on the calculation model;

and evaluating the structural load data based on a cluster analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement.

2. The flexible wing structural load assessment method of claim 1, wherein noise reduction processing is performed on the structural test data by a weighted filtering method, comprising:

selecting the structural test data through a window;

determining filtering parameters based on influencing factors of the structural test data in an actual structural test process;

and carrying out weighting processing on the structural test data based on the filtering parameters, and removing noise data of the structural test data.

3. The flexible wing structural load assessment method of claim 2, wherein weighting the structural test data comprises:

carrying out structuring treatment on the structural test data to obtain a structural test data distribution diagram;

determining coordinates of data points corresponding to each structural test data in the structural test data distribution diagram;

determining a theoretical center point of the structural test data based on the calculated data of the structural design stage;

weighting each data point based on the theoretical center point through a Gaussian distribution method;

and convolving each data point based on the filtering parameters, and determining and removing all noise data points in the data points according to convolution results.

4. A method of assessing a structural load of a flexible wing as claimed in claim 3, wherein said convolving each of said data points comprises:

and accumulating weighted sums of all the data points in a convolution circle with the size of a convolution kernel as a diameter by taking the coordinate of each data point as a center, dividing the accumulated result by the weighted sum of the convolution circle, judging whether the calculated result is smaller than a set value, and if so, determining the data point as a noise data point.

5. The flexible wing structural load assessment method of claim 1, wherein establishing the flexible wing aerodynamic load distribution model comprises:

calculating wing aerodynamic load distribution data corresponding to different deformation states of the flexible wing in a maneuvering process based on an aerodynamic elasticity calculation example and the deformation data;

dispersing all the pneumatic load distribution data based on a finite element method to obtain wing pneumatic load distribution forms corresponding to typical deformation conditions;

integrating the discrete results and defining wing aerodynamic characteristics based on the local incoming flow angle of attack and sideslip angle;

acquiring regression characteristics of aerodynamic load dependent variables and the wing aerodynamic characteristics based on a polynomial regression model;

correcting the polynomial regression model based on a local overload factor and the flight dynamics;

and establishing the flexible wing pneumatic load distribution model based on the wing pneumatic load distribution form, the regression characteristic and the modified polynomial regression model.

6. The flexible wing structure load assessment method of claim 5, wherein the local overload factor comprises:

superposition of translational overload caused by gravity center linear acceleration difference and rotational overload caused by angular acceleration difference;

an aircraft center of gravity overload is calculated based on the wing aerodynamic load profile, and local overload factors at each finite element of the flexible wing are determined based on the aircraft center of gravity overload and the flight dynamics.

7. The flexible wing structure load assessment method of claim 1, wherein the cluster analysis method comprises:

k means clustering algorithm;

and calculating key profile load data in the structural load data based on the K-means clustering algorithm, and further judging whether the structural load data amount in a clustering group in the clustering result is smaller than a set structural load data amount threshold value, if so, the maneuvering process and load distribution corresponding to the structural load data in the clustering group are problematic.

8. An electronic device, the electronic device comprising:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the flexible wing structure load assessment method of any one of claims 1-7.

9. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the flexible wing structure load assessment method of any of claims 1-7.

10. A flexible wing structure load assessment device, comprising:

the acquisition and noise reduction processing module is used for acquiring structural test data of the flexible wing and carrying out noise reduction processing on the structural test data;

the first building module is used for building a flexible wing deformation model based on the structural test data after the noise reduction treatment is completed, and obtaining deformation data of the flexible wing;

the second building module is used for building a flexible wing pneumatic load distribution model based on the deformation data and the regression model;

the combination and calculation module is used for combining the flexible wing deformation model and the flexible wing pneumatic load distribution model to obtain a flexible wing maneuvering process structural load distribution calculation model, and calculating structural load data of the flexible wing in the maneuvering process based on the calculation model;

and the evaluation and correction module is used for evaluating the structural load data based on a cluster analysis method, and judging whether to correct the calculation model according to the evaluation result until the evaluation result meets the design requirement.