Variable stiffness composite material wing aeroelastic cutting method containing multi-source uncertainty

Abstract

The invention discloses a variable stiffness composite material wing aeroelastic cutting method containing multi-source uncertainty, which comprises the following steps: S1, according to the actual parameter condition of a laying layer in a wing structure, obtaining the laying layer angle, the laying layer thickness and the area of the front section and the rear section of a wing; s2, taking the minimum weight of the wing structure as an optimization target; the layer thickness, the layer angle and the sectional area of the front and rear beams of the wing are used as optimization vectors; wingtip displacement, a wingtip torsion angle, flutter damping, structural weight and reliability are used as optimization constraints, and iterative optimization is performed through a genetic algorithm until an optimization result is converged or the number of iterations reaches a specified algebra. By the adoption of the technical scheme, aeroelastic tailoring containing multi-source uncertainty for the variable-stiffness composite material wings is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of aeroelastic analysis, and in particular relates to an aeroelastic tailoring method for a variable stiffness composite material wing containing multi-source uncertainties. Background Art

[0002] For the aeroelastic tailoring problem of variable stiffness composite materials with multi-source uncertainties, most of the existing engineering methods are based on the safety factor method. The aeroelastic tailoring methods of variable stiffness composite materials are mainly based on the low-order panel method, PK method and optimization algorithm.

[0003] Lightweight structure is an important indicator requirement in the aircraft development process. For aircraft parts made of composite materials, aeroelastic tailoring is often used in engineering to optimize the structural parameters to achieve the purpose of improving structural strength and reducing structural weight. On the basis of deterministic tailoring optimization, the uncertain factors from the composite material processing process and the service environment are quantitatively analyzed, and the reliability analysis results are introduced into the tailoring optimization constraints, which can further improve the quality of the optimization results and reduce the redundancy effect.

[0004] Compared with common linear fiber composite materials, the reinforcing fiber bundles inside the ply of variable stiffness composite materials can be laid along the designed curved path. The designability of the fiber laying path makes the stiffness of the laminate change along the direction of the fiber angle change. It has certain advantages over linear fiber composite materials in terms of anti-buckling and weight reduction design, and has good application prospects in large aspect ratio wing structures. Limited by current processing technology, there is inevitably a certain angle deviation between the actual laying path of the reinforcing fiber bundles and the theoretical design path. The actual service environment of the aircraft also has a certain influence on the properties of the composite material such as Young's modulus. Under the combined effect of uncertain factors from the processing process and the service environment, the actual performance of the variable stiffness composite structure on the aircraft will deviate from the theoretical design value to a certain extent. In severe cases, it may even affect the safety of the aircraft. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for aeroelastic tailoring of variable stiffness composite wings containing multi-source uncertainties, to achieve aeroelastic tailoring of variable stiffness composite wings containing multi-source uncertainties, to verify its effectiveness and ensure the accuracy of the analysis results while achieving weight reduction design of the structure.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A variable stiffness composite wing aeroelastic tailoring method containing multi-source uncertainty, comprising:

[0008] Step S1, according to the actual parameters of the ply in the wing structure, obtain the ply angle, ply thickness and the front and rear cross-sectional areas of the wing;

[0009] Step S2, taking the minimum weight of the wing structure as the optimization target; taking the ply thickness, ply angle and the cross-sectional area of ​​the front and rear beams of the wing as the optimization vectors; taking the wingtip displacement, wingtip twist angle, flutter damping, structural weight and reliability as the optimization constraints, performing iterative optimization through a genetic algorithm until the optimization result converges or the number of iterations reaches a specified number of generations.

[0010] Preferably, in step S2, the required wingtip displacement, wingtip twist angle, flutter damping, and structural weight response variable values ​​are obtained according to the optimization vector; according to the constraints and the individual response variable values, the influence of uncertain factors is quantified based on the generalized probability density evolution equation and the Bernstein polynomial method, and the reliability of the current individual structure is calculated; and the objective function value is returned based on the constraint response value and reliability value of the current individual.

[0011] Preferably, in step S2, the corresponding structural information is updated to the corresponding bdf file according to the optimization vector, and the bdf file is called to perform static aeroelasticity and flutter analysis to obtain the required wingtip displacement, wingtip twist angle, flutter damping, and structural weight response variable values; or according to the optimization vector, the trained Kriging model is called to directly generate the required response variable values.

[0012] Preferably, if the wingtip displacement, wingtip twist angle, flutter damping and structural weight response of the current individual all meet the optimization constraint requirements and their reliability is greater than 90%, the actual weight of the current individual is returned as the objective function value, otherwise a value 10 times the actual weight of the individual is returned as the objective function value; if the optimization result reaches the convergence condition, the iteration is stopped and the optimal result value is returned, otherwise the optimization parameters are regenerated, the bdf file recording the structural information is rewritten, and the optimization iteration is continued until the optimization converges or the set maximum optimization generation is reached.

[0013] The present invention utilizes the prominent anisotropic characteristics of composite materials to control the aeroelastic deformation of the composite lifting surface structure. In the aeroelastic tailoring process, parameters that can affect the directionality of stiffness can be selected as optimization design variables to control the aeroelastic deformation of the lifting surface and improve its flight performance, such as: composite material ply order, thickness, ply direction and ply angle, etc. The generalized probability density evolution equation is used to quantify the influence of random uncertain factors in the tailoring process, the Bernstein polynomial is used to quantify the influence of interval uncertain factors in the tailoring process, and the quantified results of the influence of uncertain factors are used to perform reliability analysis. The reliability analysis results are used as a constraint in the optimization process to improve the rationality of the optimization results and reduce the influence of redundant design. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0015] Figure 1 The present invention is a flow chart of a method for aeroelastic tailoring of a variable stiffness composite wing with multi-source uncertainty according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Embodiment 1:

[0019] like Figure 1 As shown, an embodiment of the present invention provides a variable stiffness composite wing aeroelastic tailoring method containing multi-source uncertainty, comprising:

[0020] Step S1, according to the actual parameters of the ply in the wing structure, obtain the ply angle, ply thickness and the front and rear cross-sectional areas of the wing;

[0021] Step S2, taking the minimum weight of the wing structure as the optimization target; taking the ply thickness, ply angle and the cross-sectional area of ​​the front and rear beams of the wing as the optimization vectors; taking the wingtip displacement, wingtip twist angle, flutter damping, structural weight and reliability as the optimization constraints, performing iterative optimization through a genetic algorithm until the optimization result converges or the number of iterations reaches a specified number of generations.

[0022] As an implementation method of an embodiment of the present invention, in step S2, the minimum weight of the wing structure is set as the optimization target; after the structural parameters of each generation of optimized individuals (the parameters of the first individual of the first generation are manually given) are determined, the corresponding structural information is updated to the corresponding bdf file according to the optimization vector, and the bdf file is called to perform static aeroelasticity and flutter analysis to obtain the required wingtip displacement, wingtip twist angle, flutter damping, and structural weight response variable values; or according to the optimization vector, the trained Kriging model is called to directly generate the required response variable values; according to the constraints and the individual response variable values, the influence of uncertain factors is quantified based on the generalized probability density evolution equation and the Bernstein polynomial method, and the reliability of the current individual structure is calculated; the objective function value is returned based on the constraint response value and the reliability value of the current individual; wherein, during the optimization process, the ply parameters and the front and rear beam cross-sectional areas of the wing are continuously adjusted, and if the optimal result in the current generation satisfies both the optimization requirements and the reliability requirements, it is considered that the optimization has obtained the optimal result, otherwise the optimization is continued to be iterated until the optimal result is obtained or the maximum optimization generation is reached.

[0023] The calculation process of the individual reliability of the current structure is:

[0024] Since the random uncertainty variables and interval uncertainty variables involved in this method are independent of each other, when calculating the reliability of mixed uncertainty problems, we first divide the sample space according to the specific problem to obtain n sample points of the random problem, and then determine the initial value of the random vector based on the sample point information in the random problem. in is the random uncertainty parameter involved in the mixed uncertainty problem (incoming flow velocity, air density, physical properties of materials, etc.); m is the number of random uncertainty parameters; j = 1, 2, ..., n is the sequence number of the random problem sample point. Determine the initial value of the random vector After that, the mixed uncertainty problem degenerates into an interval uncertainty problem. For the parameters that need to be calculated for reliability in the aeroelastic tailoring process of the variable stiffness composite wing, (the maximum real root of the characteristic equation in the flutter problem, wingtip displacement, wingtip twist angle, etc.), first use the Bernstein polynomial to calculate its uncertainty parameter α only in the interval in The upper boundary value of the interval response under the influence and the lower boundary value There are different response thresholds λ for different problems cr For example, for the flutter problem, the maximum real part response critical value λ of the characteristic root of the characteristic equation cr is 0; for problems that require consideration of the maximum displacement of the wing tip, the acceptable maximum displacement of the structure is the critical value of the response. Different failure intervals and safety intervals can be defined for different analysis variables. For example, for flutter problems, it is considered that is the failure interval, is a safe range. and λ cr The limit state function L is calculated by the following formula:

[0025]

[0026] For flutter, maximum wingtip displacement and maximum wingtip torsion angle problems, the structure is considered reliable when L>0 and unreliable when L<0. The non-probabilistic reliability R of such problems can be calculated by the following formula:

[0027]

[0028] By looping, calculate each random sample point After the non-probabilistic reliability R is obtained, the probability density function p(R) of R can be solved by the probability density evolution method. Then the flutter reliability metric η under random-interval mixed uncertainty conditions can be defined as:

[0029]

[0030] The ply parameters and wing front and rear beam section parameters obtained after aeroelastic tailoring design can simultaneously meet certain reliability constraints of wingtip displacement, wingtip twist angle, flutter damping and structural weight, while the optimization goal is to minimize the total mass of the wing structure. The optimized structure has a structural reliability of more than 99% while ensuring that the deformation and flutter constraints are met, and the total mass of the wing structure is reduced by more than 10%.

[0031] As an implementation method of an embodiment of the present invention, in step S2, the initial parameters of the cross-sectional area structure of the composite material layer and the front and rear beams of the wing are given in the bdf file recording the wing structure information according to engineering experience and actual design requirements; the flight state parameters involved in the aeroelastic analysis such as flight speed, atmospheric density, and angle of attack are set in the bdf file storing the calculation condition information; the parameters required by the GA toolbox, the specific parameters of the interval uncertainty variables and the random uncertainty variables, and the relevant solution parameter values ​​are set in the Matlab program. Before the optimization starts, in order to improve the iteration efficiency of the optimization algorithm, the Kriging model of the aeroelastic response variable is trained. The input set required for training the Kriging model is the optimization vector (layer thickness, layer angle, and cross-sectional area of ​​the front and rear beams of the wing) in the optimization process, and the output set is the required aeroelastic response variables (wingtip displacement, wingtip twist angle, flutter damping, and structural weight). After the input file is ready and the calculation parameters are set, the genetic algorithm optimization module is entered. During the optimization process, the trained Kriging model is first used in the Matlab program to calculate the required aeroelastic response values, and these response values ​​are used as the analysis objects of the optimization constraints. The reliability of the wingtip displacement, wingtip twist angle, flutter damping, and structural weight results are calculated based on the generalized probability density evolution method and Bernstein polynomials. If the wingtip displacement, wingtip twist angle, flutter damping, and structural weight response of the current individual meet the optimization constraint requirements and their reliability is greater than 90%, the actual weight of the current individual is returned as the objective function value, otherwise 10 times the actual weight of the individual is returned as the objective function value. If the optimization result reaches the convergence condition, the iteration is stopped and the optimal result value is returned, otherwise the optimization parameters are regenerated, the bdf file recording the structural information is rewritten, and the optimization iteration is continued until the optimization converges or the maximum optimization generation set is reached. After the optimization is completed, the weight, wingtip deformation, and reliability information can be read in the program running interface; the specific parameters of the ply and the length of the front and rear sections of the wing are read in the corresponding bdf file.

[0032] The advantages of the embodiments of the present invention are:

[0033] (1) The calculated results of the mechanical response and structural displacement of each generation are consistent with the existing results of the engineering algorithm under deterministic conditions, which proves the correctness and effectiveness of the aeroelastic analysis method used.

[0034] (2) The results of the uncertainty quantification analysis and reliability calculation methods are basically consistent with the analysis results of the classical Monte Carlo method, and the calculation efficiency is higher than the Monte Carlo method, which proves the accuracy and efficiency of the reliability constraint calculation method.

[0035] (3) In terms of optimization of variable stiffness composite wings, the structural weight is reduced through multi-generation optimization of genetic algorithms, and a more reasonable layup design and front and rear beam section design can be obtained, achieving the goal of lightweight design of the wing structure, which is in line with engineering practice.

[0036] (4) By quantitatively analyzing the uncertainties in the processing and service environment, reliability constraints are added during the optimization process. Compared with the traditional safety factor method, this method can reduce the redundancy of structural parameters in the optimization results while achieving the optimization goals.

[0037] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A variable stiffness composite wing aeroelastic tailoring method containing multi-source uncertainty, characterized in that: include: Step S1, according to the actual parameters of the ply in the wing structure, obtain the ply angle, ply thickness and the front and rear cross-sectional areas of the wing; Step S2, taking the minimum weight of the wing structure as the optimization target; The ply thickness, ply angle and cross-sectional area of ​​the front and rear beams of the wing are used as optimization vectors; the wingtip displacement, wingtip twist angle, flutter damping, structural weight and reliability are used as optimization constraints. An iterative optimization is performed using a genetic algorithm until the optimization result converges or the number of iterations reaches a specified number of generations.

2. The variable stiffness composite wing aeroelastic tailoring method with multi-source uncertainty as claimed in claim 1, characterized in that: In step S2, the required wingtip displacement, wingtip twist angle, flutter damping, and structural weight response variable values ​​are obtained according to the optimization vector; according to the constraint conditions and the individual response variable values, the influence of uncertain factors is quantified based on the generalized probability density evolution equation and the Bernstein polynomial method, and the reliability of the current individual structure is calculated; the objective function value is returned based on the constraint response value and reliability value of the current individual.

3. The variable stiffness composite wing aeroelastic tailoring method with multi-source uncertainty as claimed in claim 2, characterized in that: In step S2, the corresponding structural information is updated to the corresponding bdf file according to the optimization vector, and the bdf file is called to perform static aeroelasticity and flutter analysis to obtain the required wingtip displacement, wingtip twist angle, flutter damping, and structural weight response variable values; Alternatively, based on the optimization vector, the trained Kriging model is called to directly generate the required response variable value.

4. The variable stiffness composite wing aeroelastic tailoring method with multi-source uncertainty as claimed in claim 3, characterized in that: If the wingtip displacement, wingtip twist angle, flutter damping and structural weight response of the current individual meet the optimization constraint requirements and their reliability is greater than 90%, the actual weight of the current individual is returned as the objective function value, otherwise 10 times the actual weight of the individual is returned as the objective function value; if the optimization result reaches the convergence condition, the iteration is stopped and the optimal result value is returned, otherwise the optimization parameters are regenerated, the bdf file recording the structural information is rewritten, and the optimization iteration is continued until the optimization converges or the set maximum optimization generation is reached.

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