Wing type optimization design method for split laminated wing

Abstract

The invention belongs to the technical field of aircraft design, and particularly relates to a split type laminated wing type optimization design method. According to the design method, split design is adopted, a mean camber line is used for dividing a single reference airfoil into an upper decomposed airfoil and a lower decomposed airfoil, a Hicks-Henne type function parameterization method is used for conducting parameterization deformation on the mean camber line, B-spline control points are arranged, and after hybrid parameterization, the mean camber line is divided into an upper decomposed airfoil and a lower decomposed airfoil. And a multi-island genetic algorithm is matched with an airfoil aerodynamic performance prediction tool NeuralFoil based on a neural network to carry out optimization analysis on the airfoil aerodynamic performance, and finally the optimal stacked wing type meeting the design requirement is obtained. According to the design method, the design space range and the optimization efficiency in the airfoil optimization process can be considered.

Description

Technical Field

[0001] This invention belongs to the field of aircraft design technology, and in particular relates to a method for optimizing the design of split-type stacked wing airfoils. Background Technology

[0002] With the increasing complexity of UAV flight environments and missions, wide-speed-range high-efficiency flight capability and multi-mission adaptability have become core requirements for UAV design, and achieving "one aircraft with multiple functions" has become a trend in UAV design. High-speed cruise-low-speed loitering missions require aircraft to have efficient cruise capability across a wide speed range. Variant aircraft achieve cross-condition performance optimization by dynamically adjusting their aerodynamic shape, and can change wing geometry characteristics according to environmental parameters during flight, thereby expanding the flight envelope and improving overall efficiency.

[0003] Aircraft used for reconnaissance or electronic warfare missions need to approach the mission area at high speed and then perform the mission at low speed. This requires the aircraft to have both high-speed cruise and low-speed maneuvering capabilities. Generally, a single-layout fixed-wing aircraft cannot meet the design requirements of two cruise points with significant speed differences. Therefore, using variator technology to adapt to different operating conditions becomes a feasible approach. Under typical mission requirements, these aircraft usually do not need to fly continuously over a wide speed range, but rather focus on aerodynamic performance in two discrete states: high speed and low speed. Therefore, significantly reducing cruise speed by adjusting the effective wing area has practical application value. This leads to the proposal of a stacked wing variator concept, consisting of upper and lower wings. During low-speed flight, the upper and lower wings decompose into a biplane configuration, increasing the wing area to provide sufficient lift and improve the lift-to-drag ratio at low speeds. Simultaneously, the beneficial interwing interference of the biplane configuration enhances the aircraft's maneuverability. During high-speed flight, the upper and lower wings merge into a single-wing configuration to reduce drag, giving the aircraft excellent high-speed cruise performance.

[0004] According to the design scheme of folded-wing variant aircraft, the airfoil geometry requires the combined airfoil to have sufficient thickness to ensure that the thickness of the decomposed airfoil meets structural strength requirements. The upper and lower airfoils need to have high geometric matching to ensure a perfect fit and avoid aerodynamic performance losses caused by gaps or irregular shapes. In terms of aerodynamic performance, the decomposed airfoil needs excellent low-speed aerodynamic characteristics to achieve high lift and a high lift-to-drag ratio, while the combined airfoil must also meet the requirements of low drag and a high lift-to-drag ratio during high-speed flight. Therefore, a design methodology that comprehensively considers these design requirements is urgently needed for folded-wing airfoil design. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a partitioned stacked airfoil optimization design method. This method adopts a partitioned design, using dividing lines to divide a single reference airfoil into upper and lower decomposed airfoils. It uses a hybrid parameterization method and a neural network-based airfoil aerodynamic performance prediction tool, which can balance the design space range and optimization efficiency in the optimization solution process.

[0006] The technical solution of the present invention is as follows:

[0007] A method for optimizing the design of split-type stacked airfoils includes the following steps:

[0008] Step 1: Select a reference airfoil and calculate the mid-curve of the airfoil;

[0009] Step 2: Parameterize the mid-curve of the airfoil using the improved Hicks-Henne type function method to obtain the B-spline control point reference line;

[0010] Step 3: Arrange quasi-uniform B-spline control points on the leading edge of the lower surface of the reference airfoil and the B-spline control point reference line to obtain the quasi-uniform B-spline curve;

[0011] Step 4: Using the quasi-uniform B-spline curve as the dividing line, obtain the stacked airfoil and perform airfoil aerodynamic analysis;

[0012] Step 5: Using the weights of each type of function and the abscissa of the quasi-uniform B-spline control points as design variables, set the aerodynamic optimization objective and constraints, and perform global optimization through a multi-island genetic algorithm to obtain the optimal stacked airfoil that meets the design requirements.

[0013] Preferably, step 1 specifically includes:

[0014] Based on the design requirements of the stacked wing variant aircraft in the combined state, a reference airfoil with aerodynamic performance and thickness that meet the requirements is selected. Non-uniform rational spline curves are used to interpolate the reference airfoil data. By traversing the x-coordinate of the reference airfoil and interpolating to calculate the average y-coordinate of the upper and lower wing surfaces, the mid-arc coordinates of the airfoil are obtained.

[0015] Preferably, the improved Hicks-Henne type function expression is:

[0016]

[0017] Among them, f k (x) represents the type function, x represents the x-coordinate, k represents the k-th type function, n represents the number of type functions, e(k) is an intermediate process quantity, and α and β are both coefficients. k This represents the node between the leading edge and trailing edge of the airfoil.

[0018] Preferably, the expression for the B-spline control point reference line is:

[0019]

[0020] Where y represents the ordinate of the airfoil curve after deformation, y0 represents the ordinate of the airfoil curve before deformation, and c k Weights of representational functions.

[0021] Preferably, the expression for the quasi-uniform B-spline curve in step 3 is:

[0022]

[0023] in, Let n represent the coordinate vector of a point on the quasi-uniform B-spline curve, and n represent the control points of the quasi-uniform B-spline. Quantity, B i,d (t) represents the basis function of the B-spline curve, d represents the degree of the curve, and t is the parameter of the parametric equation. min t max This represents the minimum and maximum values ​​of t.

[0024] Preferably, the airfoil aerodynamic analysis in step 4 uses NeuralFoil, a neural network-based airfoil aerodynamic performance prediction tool.

[0025] Preferably, the aerodynamic optimization objective in step 5 is the weighted sum of the lift-to-drag ratio under various target lift coefficients of the stacked airfoil.

[0026] Preferably, the augmented objective function for global optimization in step 5 is expressed as:

[0027] Max{∑w i ·K i -∑α·g i}

[0028]

[0029] Among them, w i K represents the weight under operating condition i. i Let g be the overall lift-to-drag ratio of the upper and lower wings under operating condition i, α be the penalty function coefficient, and g be the weight of the upper and lower wings. i C is a function representing the situation exceeding the constraint range under operating condition i. L_upper_i C represents the lift coefficient of the upper wing airfoil under operating condition i. D_upper_i This indicates the drag coefficient of the upper wing airfoil. C represents the dimensionless length of the lower wing airfoil. L_lower_i C represents the lift coefficient of the lower wing airfoil under operating condition i. D_lower_i This represents the drag coefficient of the lower wing airfoil under operating condition i.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] 1. The split-type stacked airfoil optimization design method proposed in this invention uses split design, which divides a single reference airfoil into two decomposed airfoils by dividing lines, which can effectively simplify the stacked airfoil optimization design problem.

[0032] 2. The split-type stacked airfoil optimization design method proposed in this invention adopts a hybrid parameterization approach, which not only meets the design requirements of stacked airfoils but also effectively expands the airfoil design space.

[0033] 3. The segmented stacked airfoil optimization design method proposed in this invention uses a global optimization algorithm and a neural network-based airfoil aerodynamic performance prediction tool, which takes into account both optimization efficiency and optimization effect. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the accompanying drawings. The accompanying drawings are schematic and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the combined upper and lower wings of a stacked wing variant aircraft.

[0036] Figure 2 This is a schematic diagram of the disassembled upper and lower wings of a stacked wing variant aircraft.

[0037] Figure 3 This is a flowchart of the split-layered airfoil optimization design method of the present invention.

[0038] Figure 4 This is a schematic diagram of a stacked wing airfoil.

[0039] Figure 5 This is a schematic diagram of the optimized decomposed airfoil segmentation lines.

[0040] Figure 6 It is a curve showing the relationship between the lift coefficient and the drag coefficient before and after optimization. Detailed Implementation

[0041] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0043] The proposed method for optimizing the design of segmented stacked wing airfoils involves several steps. First, based on the design requirements of the stacked wing variant aircraft in its combined state, a baseline airfoil with superior aerodynamic performance and meeting thickness requirements is selected, and its mid-arc line data is calculated. This mid-arc line is then used as the baseline dividing line for airfoil segmentation. Based on the baseline airfoil, an improved Hicks-Henne type function parameterization method is used to parametrically deform the mid-arc line. The deformed mid-arc line serves as the control line, and quasi-uniform B-spline control points are arranged on the leading edge of the lower surface of the airfoil and on the deformed mid-arc line. After parameterizing the mid-arc line, a multi-island genetic algorithm combined with the neural network-based airfoil aerodynamic performance prediction tool, NeuralFoil, is used to optimize and analyze the airfoil's aerodynamic performance. During the optimization process, the decomposed mid-arc line is used as the design variable, and the overall lift-to-drag ratio of the upper and lower airfoils is used as the optimization objective. Under low-speed conditions and a given lift coefficient, the optimal decomposed airfoil is obtained. Finally, the baseline airfoil is segmented based on the mid-arc line, and the leading edge of the decomposed airfoil is smoothed to obtain the optimized decomposed airfoil.

[0044] Based on the high-speed cruise and low-speed cruising mission requirements of the variant stacked-wing aircraft, the aerodynamic performance of the airfoil under both low-speed and high-speed conditions needs to be comprehensively considered during the design of the stacked-wing airfoil. The combined stacked-wing configuration is more suitable for high-speed conditions; therefore, the selection of the reference airfoil primarily considers its aerodynamic performance under high-speed conditions. The lift coefficient and Reynolds number of the aircraft under high-speed conditions can be calculated based on parameters such as wing loading determined during the overall aircraft design phase, and the reference airfoil can be selected accordingly. The reference airfoil can be selected from an existing airfoil library based on design requirements, or the optimal reference airfoil can be obtained using optimization design methods. During optimization or selection, it is necessary to ensure that the reference airfoil has sufficient thickness to ensure that the wing in its decomposed state meets structural strength requirements.

[0045] After selecting a reference airfoil and obtaining airfoil data, in order to ensure calculation accuracy, non-uniform rational B-spline (NURBS) curves are used to interpolate the airfoil data. By traversing the x-coordinate of the airfoil and interpolating to calculate the average y-coordinate of the upper and lower airfoil surfaces, the y-coordinate of the point on the arc of the airfoil can be obtained.

[0046] After calculating the airfoil mid-curve coordinates, the airfoil mid-curve is parameterized using a hybrid parameterization method. First, the mid-curve is deformed using an improved Hicks-Henne shape function method. The Hicks-Henne shape function method obtains a new mid-curve by superimposing a shape function onto the baseline airfoil curve. The improved Hicks-Henne shape function expression is as follows:

[0047]

[0048] Here, the type function at k=n is used to control the rapid decay of the function far from the trailing edge. The coefficient α controls the change in slope, and the coefficient β controls the decay rate. Preferably, α=10 and β=8. k For nodes selected between the leading and trailing edges of the airfoil (including the leading and trailing edges), 0 = x1 < x2 < ... x k <...x n =1, the number of selected nodes is equal to the number of basis functions.

[0049] After determining the various functions according to the above formula, superimposing each function with different weights onto the baseline airfoil curve yields the new central arc. Using the weights of each function as design variables effectively controls the curve shape.

[0050]

[0051] Using the mid-arc line deformed by the Hicks-Henne type function method as the control line, quasi-uniform B-spline control points are arranged on the leading edge of the lower surface of the airfoil and the deformed mid-arc line. The quasi-uniform B-spline has the property that the tangent at the endpoint is the line connecting the two reciprocal control points, which makes it easier to control the tangent direction of the airfoil segmentation line at the endpoint.

[0052] The equation of the B-spline curve is:

[0053]

[0054] Where the basis functions of the B-spline curve are:

[0055]

[0056] Where k represents the number of identical nodes at both ends, i.e., the repetition degree, and u represents the parameter of the parametric equation. k and u k+1 The nodes of the curve are defined by equal coefficients, using a node vector. For a quasi-uniform B-spline curve, the nodes at both ends have a repetition degree k, and the nodes in the middle are a uniformly increasing sequence.

[0057] Using the abscissas of the quasi-uniform B-spline control points as design variables, with the initial control point located at the leading edge of the lower airfoil and subsequent control points situated on the deformed mid-curve, the ordinates can be calculated via interpolation from the abscissas of the control points, thus allowing the determination of the coordinates of the entire quasi-uniform B-spline curve. This hybrid parameterization method effectively ensures sufficient design space during airfoil optimization while also resulting in similar thicknesses for the upper and lower airfoils and smooth segmentation lines. Smoothing the leading edge of the decomposed airfoil yields the airfoil model suitable for aerodynamic analysis.

[0058] In terms of optimizing aerodynamic performance, NeuralFoil, a neural network-based airfoil aerodynamic performance prediction tool, was used to analyze the airfoil's aerodynamic performance. According to the design requirements of the decomposed state of the laminated wing airfoil, the airfoil optimization design in this state mainly aims to improve the lift-to-drag ratio under low-speed flight conditions and high lift coefficients. However, selecting a single design point as the optimization objective can lead to excessive degradation of other flight condition design points. Therefore, in the decomposed optimization of the laminated wing, in addition to selecting high lift coefficient flight conditions, it is also necessary to select low lift coefficient flight conditions and assign appropriate objective weights. Thus, in the optimization of the laminated wing airfoil, the weighted sum of the lift-to-drag ratios under each objective lift coefficient flight condition is used as the optimization objective. When calculating the total aerodynamic coefficients of the upper and lower wings, it is important to note that in the decomposed state of the stacked wing design, the upper and lower airfoils operate at the same angle of attack. Therefore, in the aerodynamic calculation of the decomposed state, the aerodynamic forces of the upper and lower airfoils are calculated separately, and then the aerodynamic forces of the upper and lower airfoils at each angle of attack are added together to obtain the total aerodynamic force in the decomposed state. However, the leading edge point of the dividing line in this invention is not fixed at the leading edge point of the airfoil, which makes the length of the lower wing airfoil different from the reference airfoil. Therefore, when calculating the total aerodynamic coefficients of the upper and lower wings, the aerodynamic coefficient of the lower wing needs to be multiplied by its own dimensionless length before being added together. NeuralFoil can also provide moment coefficients, maximum lift coefficients, etc., and corresponding constraints can be added as needed during optimization design.

[0059] Considering the constraints, the augmented objective function optimized using the penalty function method can be expressed as:

[0060] Max{Σw i ·K i -Σα·g i} (7)

[0061]

[0062] Among them, w i K represents the weight under operating condition i. i Let g be the total lift-to-drag ratio of the upper and lower wings under operating condition i, where α is the penalty function coefficient, which is generally taken as a large value to prioritize the satisfaction of constraints. i C is a function representing the situation exceeding the constraint range under operating condition i. It is generally expressed as the absolute value of the difference between the constraint quantity and its upper and lower limits. L_upper_iC represents the lift coefficient of the upper wing airfoil under operating condition i. D_upper_i This indicates the drag coefficient of the upper wing airfoil. C represents the dimensionless length of the lower wing airfoil. L_lower_i C represents the lift coefficient of the lower wing airfoil under operating condition i. D_lower_i This represents the drag coefficient of the lower wing airfoil under operating condition i.

[0063] After processing the design variables, optimization objectives and constraints, a multi-island genetic algorithm is used for global optimization to find the global optimal solution within the feasible region, thus obtaining the optimal stacked airfoil that meets the design requirements.

[0064] Example 1

[0065] Choosing the GOE 600 airfoil as the baseline airfoil, the front and rear airfoils were optimized as follows: Figure 4 , Figure 5 As shown in Table 1, the optimization results are compared. The optimized airfoil improves the lift-to-drag ratio by 169.7% at a high lift coefficient CL = 1.8, and only decreases by 27.5% at a lift coefficient CL = 0.6. This achieves the design goal of significantly improving the lift-to-drag ratio of the upper and lower airfoils under high lift coefficients while ensuring limited degradation under low lift coefficient conditions. The polar curves of the upper and lower airfoils before and after optimization in the three-dimensional flow state are compared as follows: Figure 6 As shown in the figure. The calculation results indicate that within the lower lift coefficient range, there is no significant difference in the lift-drag characteristics of the aircraft before and after optimization. However, under high lift coefficients, the drag coefficient of the optimized aircraft is significantly reduced. Under the high lift coefficient condition CL=1.8, the optimized aerodynamic shape improves the lift-drag ratio by 18.4%. It should be noted that due to aerodynamic interference caused by the spanwise variation of the upper and lower wing spacing, the three-dimensional model aerodynamic numerical simulation results show a smaller increase in lift-drag ratio compared to the two-dimensional ideal case. However, the airfoil optimization design method based on two-dimensional flow solutions effectively reduces drag under high lift target conditions, indicating that the proposed stacked wing partitioned optimization design method has engineering application value.

[0066] Table 1 Comparison of aerodynamic characteristics before and after airfoil optimization.

[0067]

[0068] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0069] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0070] In this invention, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "multiple" refers to two or more unless otherwise expressly defined.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of a split-type stacked airfoil, comprising the following steps: Step 1: Select a reference airfoil and calculate the mid-curve of the airfoil; Step 2: Parameterize the mid-curve of the airfoil using the improved Hicks-Henne type function method to obtain the B-spline control point reference line; Step 3: Arrange quasi-uniform B-spline control points on the leading edge of the lower surface of the reference airfoil and the B-spline control point reference line to obtain the quasi-uniform B-spline curve; Step 4: Using the quasi-uniform B-spline curve as the dividing line, obtain the stacked airfoil and perform airfoil aerodynamic analysis; Step 5: Using the weights of each type of function and the abscissa of the quasi-uniform B-spline control points as design variables, set the aerodynamic optimization objective and constraints, and perform global optimization through a multi-island genetic algorithm to obtain the optimal stacked airfoil that meets the design requirements.

2. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, Step 1 specifically includes: Based on the design requirements of the stacked wing variant aircraft in the combined state, a reference airfoil with aerodynamic performance and thickness that meet the requirements is selected. Non-uniform rational spline curves are used to interpolate the reference airfoil data. By traversing the x-coordinate of the reference airfoil and interpolating to calculate the average y-coordinate of the upper and lower wing surfaces, the mid-arc coordinates of the airfoil are obtained.

3. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, The improved Hicks-Henne type function expression is as follows: Among them, f k (x) represents the type function, x represents the x-coordinate, k represents the k-th type function, n represents the number of type functions, e(k) is an intermediate process quantity, and α and β are both coefficients. k This represents the node between the leading edge and trailing edge of the airfoil.

4. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, The expression for the B-spline control point reference line is: Where y represents the ordinate of the airfoil curve after deformation, y0 represents the ordinate of the airfoil curve before deformation, and c k Weights of representational functions.

5. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, The expression for the quasi-uniform B-spline curve in step 3 is as follows: in, Let n represent the coordinate vector of a point on the quasi-uniform B-spline curve, and n represent the control points of the quasi-uniform B-spline. Quantity, B i,d (t) represents the basis function of the B-spline curve, d represents the degree of the curve, and t is the parameter of the parametric equation. min t max This represents the minimum and maximum values ​​of t.

6. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, In step 4, the airfoil aerodynamic analysis uses NeuralFoil, a neural network-based airfoil aerodynamic performance prediction tool.

7. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, In step 5, the aerodynamic optimization objective is the weighted sum of the lift-to-drag ratio under various target lift coefficients for the stacked airfoil.

8. The method for optimizing the design of split-layered airfoils according to claim 1, characterized in that, The augmented objective function for global optimization in step 5 is expressed as: Max{∑w i ·K i -∑α·g i } Among them, w i K represents the weight under operating condition i. i Let g be the overall lift-to-drag ratio of the upper and lower wings under operating condition i, α be the penalty function coefficient, and g be the weight of the upper and lower wings. i C is a function representing the situation exceeding the constraint range under operating condition i. L_upper_i C represents the lift coefficient of the upper wing airfoil under operating condition i. D_upper_i This indicates the drag coefficient of the upper wing airfoil. C represents the dimensionless length of the lower wing airfoil. L_lower_i C represents the lift coefficient of the lower wing airfoil under operating condition i. D_lower_i This represents the drag coefficient of the lower wing airfoil under operating condition i.

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