Optimization method for free degree flutter of flying wing layout unmanned aerial vehicle

Abstract

The application discloses a kind of flying wing layout unmanned aerial vehicle body freedom degree flutter optimization method, it is related to the field of aircraft structure optimization.The form of rigid-coupled flutter is eliminated by improving the inertia of body pitch.The corresponding structure finite element model of flying wing layout unmanned aerial vehicle is established;Modal shape is calculated using finite element model and modal convergence analysis of flutter speed is carried out;Optimization variables and upper and lower limits of variable are set, and design variable is the physical parameter of structure unit;Multiple constraint functions are set, including the stress of unit, bending-torsional coupled flutter speed, divergence speed, structure mass;Objective function is set, the inertia of body pitch is maximum;According to the result file obtained by optimization, the numerical value of design variable is modified.The inertia of body pitch is increased by structure optimization, the flutter frequency is improved, and the coupling of pitch short-period mode and bending mode is avoided as much as possible, so that the purpose of eliminating body freedom degree flutter phenomenon is realized.

Description

Technical Field

[0001] This invention relates to the field of aircraft structure optimization, and specifically to a method for flutter vibration of the degrees of freedom of a flying wing layout unmanned aircraft. Background Technology

[0002] Flying-wing UAVs, with their high aspect ratio and extensive use of lightweight composite materials, significantly increase wing flexibility, leading to pronounced aeroelastic effects resulting from aerodynamic-structural coupling under aerodynamic loads. On one hand, the blended wing-body design of flying-wing aircraft reduces pitch inertia and increases pitch mode frequencies; on the other hand, the high aspect ratio lowers wing bending stiffness, reducing bending mode frequencies. Under these combined effects, short-period pitch modes are prone to coupling with bending modes, inducing flutter phenomena in the body degrees of freedom.

[0003] Current research on volume flutter largely focuses on active flutter suppression, which, while effective, is costly. Volume flutter behavior depends on the inertia and constitutive properties of the wing and fuselage. Therefore, by optimizing the stiffness distribution of flying-wing UAVs to suit their structural characteristics, volume flutter can also be eliminated. Consequently, determining the specific method for adjusting the stiffness distribution becomes a crucial technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] To address the above problems, this invention proposes an optimization method for body-degree-of-freedom flutter in a flying-wing UAV. While increasing the flutter speed, the method optimizes the flutter morphology by increasing the pitch rotation inertia of the airframe to eliminate the rigid-elastic coupling flutter morphology, thus causing the traditional bending-torsional coupling flutter morphology to occur first, thereby achieving the goal of eliminating body-degree-of-freedom flutter.

[0005] The technical solution of the present invention includes the following steps: Step 1: Draw the finite element model of the flying wing UAV in MSC NASTRAN software; Step 2: In MSC NASTRAN software, perform modal analysis and flutter calculation on the finite element model, and perform convergence analysis of modal truncation to obtain the natural modal data of the finite element model, including mode shapes and frequencies; Step 3: Use a Python program to process the mode shape results in the first 9 modes, generate a *.free file, use it as input for flutter calculation, and write the flutter calculation file; Step 4: Perform flutter analysis on the model using the g-method to obtain the flutter velocity; Step 5: Gradually increase the number of eigenvalues ​​of the flutter equation selected in the modal solution in Step 2, i.e. the modal truncation order, and repeat Step 2 to Step 4 to obtain the flutter velocity under different modal truncation orders; Step 6: Based on the modal truncation order at which the flutter velocity is achieved, determined in Step 5, this order is used as the modal order for subsequent flutter calculations, and the flutter velocity obtained at this point is used as the initial flutter velocity for the final optimization. Step 7: Use MATLAB to process the flutter calculation results from the previous step, obtain flutter equation eigenvalue data under different flow velocities, and obtain velocity and damping curves, as well as velocity and frequency curves. Step 8: Write the *.bdf input file for optimization, define the lower and upper limits of the design variables, and define the constraints and objective function; Step 9: Input the *.bdf input file into the MSC NASTRAN software and use the sequential quadratic programming algorithm in the software to optimize the structure of the finite element model established in Step 1; Step 10: Adjust the structure of the finite element model based on the results of the last iteration in the previous step; Step 11: Perform modal calculations on the adjusted finite element model to obtain the structural mode shapes again. Process the mode shape results using a Python program to generate a *.free file, which will be used as input for flutter calculation. Write a flutter calculation file and use the g-method to perform flutter analysis on the model to obtain the flutter velocity. The flutter calculation results were processed using MATLAB to obtain flutter equation eigenvalue data under different flow velocities, and to obtain velocity and damping curves, as well as velocity and frequency curves. Compare and analyze the Vg and Vf diagrams before and after optimization. If the coupled modes change, the optimization is considered successful. Otherwise, return to step eight and relax the upper and lower limits of the design variables until the optimization is successful.

[0006] Step 12: Draw the modal shapes before and after optimization.

[0007] In step eight, the design variables in the input file are defined as: the thickness attributes of the fuselage beam, fuselage skin, internal wing support structure, wing skin, and winglet shell units. The lower limit of the design variables is defined as 0.25 times the initial thickness, and the upper limit of the design variables is defined as 2.5 times the initial thickness. The constraints are defined as: the stress of the unit at the wing-fuselage connection is not greater than 0.8 times the allowable stress value of the PC-ABS material, the bending-torsional coupling flutter velocity is not less than the original velocity, the divergence velocity is not less than the original velocity, and the structural mass is not greater than the original mass. The objective function is defined as: maximizing the pitch inertia of the fuselage.

[0008] Step eight considers multiple design factors simultaneously: strength, stiffness, mass, and aeroelastic constraints under cruise conditions. When setting flutter constraints, to avoid the inconvenience of directly applying flutter velocity constraints to the optimization calculations, aeroelastic system damping constraints at specified velocities are chosen and specified within the flight envelope. This means that at typical incoming flow velocities within the flight envelope, the following conditions must be met: .

[0009] This invention increases the pitch inertia and flutter frequency through structural optimization, minimizing the coupling between short-period pitch modes and bending modes, thereby eliminating flutter in the body degrees of freedom. Its key technical point and advantage lies in optimizing the pitch inertia of the machine body by adjusting the structural unit dimensional parameters, while ensuring the rationality of other constraints and the structure. This separates the elastic bending mode from the short-period rigid body mode, thus eliminating flutter in the body degrees of freedom or causing it to occur after classical bending-torsional coupled flutter, thereby increasing the flutter velocity. Attached Figure Description

[0010] Figure 1 This is a flowchart of the method for optimizing the flutter of the degrees of freedom of a flying-wing layout unmanned aerial vehicle disclosed in this invention; Figure 2 This is an example of a flying-wing layout UAV structure finite element model in this invention; Figure 3 These are the first six elastic mode shapes obtained from modal analysis; Figure 4 These are the Vg and Vf plots of the unoptimized model obtained after processing; Figure 5 These are the Vg and Vf plots of the optimized model; Figure 6 These are flutter morphology diagrams of the model before and after optimization. Detailed Implementation

[0011] To clearly illustrate the technical features of this patent, the following detailed description is provided through specific embodiments and in conjunction with the accompanying drawings.

[0012] like Figure 1 As shown, the optimization method for flutter of the degrees of freedom of a flying-wing UAV includes the following steps: Step 1: As Figure 2 As shown, the finite element model of the flying wing UAV was drawn in MSC NASTRAN software. The structural finite element model of the flying wing UAV was established using triangular shell elements, quadrilateral triangular shell elements and solid elements. The fuselage adopts a beam frame structure, and the wing interior adopts a regular tetrahedral support structure established by shell elements.

[0013] Step 2: In MSC NASTRAN software, perform modal analysis and flutter calculations on the finite element model, and conduct convergence analysis of modal truncation to obtain the natural modal data of the finite element model, including mode shapes and frequencies. The obtained mode shapes are as follows: Figure 3 As shown in Table 1 below; Table 1

[0014] Take the first 9 modes (i.e., the 6th rigid body mode and the first 3 elastic modes) and output the mode shapes.

[0015] Step 3: Use a Python program to process the mode shape results in the first 9 modes, form a *.free file, use it as input for flutter calculation, and write the flutter calculation file.

[0016] Step 4: The model was analyzed for flutter using the g method (an improved PK method), and the flutter velocity was found to be 47.5 m / s.

[0017] Improvements to existing techniques in the PK (player versus player) system, such as the following papers: Chen, PC, “A Damping Perturbation Method for Flutter Solution: Theg-Method,” International Forum on Aeroelasticity and Structural Dynamics, Hampton, VA, Jun. 22-25, 1999. Also in AIAA Journal, Vol. 38, No. 9, Sep2000, pp. 1519-1524. Step 5: Gradually increase the number of eigenvalues ​​of the flutter equation selected in Step 2, i.e. the modal truncation order. Take the first 10 modes, the first 11 modes, the first 12 modes, the first 13 modes, the first 14 modes, the first 15 modes... the first 24 modes, the first 25 modes in sequence, and repeat Step 2 to Step 4 to obtain the flutter velocity under different modal truncation orders.

[0018] Step Six: As obtained from Step Five, when the modal cutoff order reaches 12, the chatter velocity is 49.1 m / s. The difference between this and the chatter velocity calculated when the modal cutoff order reaches 13 is less than 0.5 m / s. At this point, it is determined that the modal convergence of the chatter velocity has been achieved. The 12th mode is also the modal order used in subsequent chatter calculations. The chatter velocity obtained at this point is used as the initial chatter velocity for final optimization. The chatter calculation results are shown in the table below. Table 2

[0019] Meanwhile, the divergence velocity of the model can also be obtained by static aeroelastic analysis of the model using the g-method, which is 119 m / s.

[0020] Step 7: Use MATLAB to process the flutter calculation results from the previous step, obtaining flutter equation eigenvalue data at different flow velocities, and generating velocity-damping curves (Vg graph) and velocity-frequency curves (Vf graph), as shown below. Figure 4 As shown.

[0021] Analysis revealed that the initial model had a flutter velocity of 49.1 m / s, and the flutter mode was a volume flutter generated by the coupling of the pitch mode and the symmetrical bending mode.

[0022] Step 8: Compile the *.bdf input file for optimization. Specifically, define the design variables in the input file as: the thickness properties of the fuselage spars, fuselage skin, internal wing support structure, wing skin, and winglet shell units. Define the lower limit of the design variables as 0.25 times the initial thickness and the upper limit as 2.5 times the initial thickness. Define the constraints as: the stress of the unit at the wing-fuselage connection is not greater than 0.8 times the allowable stress value of the PC-ABS material; the bending-torsional flutter velocity is not less than the original velocity; the divergence velocity is not less than the original velocity; and the structural mass is not greater than the original mass. Define the objective function as: maximizing the pitch inertia of the fuselage.

[0023] Step 9: Input the *.bdf input file into the MSC NASTRAN software, and use the sequential quadratic programming algorithm in the software to optimize the structure of the finite element model established in Step 1. The results of each iteration in the optimization process are shown in the table below. Table 3

[0024] Table 4

[0025] Step 10: Adjust the structural mass and structural stiffness of the finite element model based on the results of the last iteration in the previous step.

[0026] Step 11: Perform modal calculations on the adjusted finite element model to obtain the structural mode shapes again. Use a Python program to process the mode shape results in the first 12 modes to form a *.free file, which will be used as the input for flutter calculation. Write a flutter calculation file and use the g method to perform flutter analysis on the model to obtain the flutter velocity. The flutter calculation results were processed using MATLAB to obtain flutter equation eigenvalue data under different flow velocities, resulting in velocity-damping curves (Vg plot) and velocity-frequency curves (Vf plot), as shown below. Figure 5 As shown.

[0027] Compare and analyze the Vg and Vf diagrams before and after optimization. If the coupled modes change, the optimization is considered successful. Otherwise, return to step eight and relax the upper and lower limits of the design variables until the optimization is successful.

[0028] Step 12: Draw the modal shape diagrams before and after optimization, such as... Figure 6 As shown.

[0029] There are many specific ways to implement this invention. The above description is only a preferred embodiment of this invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of this invention, and these improvements should also be considered within the scope of protection of this invention.

Claims

1. An optimization method for the flutter of a flying-wing unmanned aerial vehicle (UAV) airframe, characterized in that, Includes the following steps: Step 1: Draw the finite element model of the flying wing UAV in MSC NASTRAN software; Step 2: In MSC NASTRAN software, perform modal analysis and flutter calculation on the finite element model, and perform convergence analysis of modal truncation to obtain the natural modal data of the finite element model, including mode shapes and frequencies; Step 3: Use a Python program to process the mode shape results in the first 9 modes, generate a *.free file, use it as the input file for flutter calculation, and write the flutter calculation file; Step 4: Perform flutter analysis on the model using the g-method to obtain the flutter velocities of the volume degrees of freedom; Step 5: Gradually increase the number of eigenvalues ​​of the flutter equation selected in the modal solution in Step 2, i.e. the modal truncation order, and repeat Step 2 to Step 4 to obtain the flutter velocity under different modal truncation orders; Step 6: Based on the modal truncation order at which the flutter velocity is achieved, determined in Step 5, this order is used as the modal order for subsequent flutter calculations, and the flutter velocity obtained at this point is used as the initial flutter velocity for the final optimization. Step 7: Use MATLAB to process the flutter calculation results from the previous step, obtain flutter equation eigenvalue data under different flow velocities, and obtain velocity and damping curves, as well as velocity and frequency curves. Step 8: Write the *.bdf input file for optimization, define the lower and upper limits of the design variables, and define the constraints and objective function; Step 9: Input the *.bdf input file into the MSC NASTRAN software and use the sequential quadratic programming algorithm in the software to optimize the structure of the finite element model established in Step 1; Step 10: Adjust the structure of the finite element model based on the results of the last iteration in the previous step; Step 11: Perform modal calculations on the adjusted finite element model to obtain the structural mode shapes again. Process the mode shape results using a Python program to generate a *.free file, which will be used as input for flutter calculation. Write a flutter calculation file and use the g-method to perform flutter analysis on the model to obtain the flutter velocity. The flutter calculation results were processed using MATLAB program to obtain flutter equation eigenvalue data under different flow velocities, and to obtain velocity and damping curves (Vg graph) and velocity and frequency curves (Vf graph). Compare and analyze the Vg and Vf diagrams before and after optimization. If the coupled modes change, the optimization is considered successful. Otherwise, return to step eight and relax the upper and lower limits of the design variables until the optimization is successful. Step 12: Draw the modal shapes before and after optimization.

2. The method for optimizing the flutter of a flying-wing UAV body according to claim 1, characterized in that, In step eight, the design variables in the input file are defined as: the thickness attributes of the fuselage beam, fuselage skin, internal wing support structure, wing skin, and winglet shell unit. The lower limit of the design variables is defined as 0.25 times the initial thickness, and the upper limit of the design variables is defined as 2.5 times the initial thickness. The constraints are defined as: the stress of the unit at the wing-fuselage connection is not greater than 0.8 times the allowable stress value of the PC-ABS material, the bending-torsional coupling flutter velocity is not less than the original velocity, the divergence velocity is not less than the original velocity, and the structural mass is not greater than the original mass. The objective function is defined as: maximizing the pitch inertia of the aircraft.

3. The method for optimizing the flutter of a flying-wing UAV body according to claim 1, characterized in that, Step eight considers multiple design factors simultaneously: strength, stiffness, mass, and aeroelastic constraints under cruise conditions. When setting flutter constraints, to avoid inconvenience to optimization calculations caused by directly applying flutter velocity constraints, the aeroelastic system damping constraint at a specified velocity is chosen and specified within the flight envelope. This means that at typical incoming flow velocities within the flight envelope, the following conditions must be met: .

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