A BAYC-alpha source term model-based leaf vortex generator design method

Abstract

The application relates to a BAYC-alpha source term model-based design method of a leaf vortex generator, and belongs to the technical field of aircraft design and flow control. The method improves an existing BAYC source term model, forms a new BAYC-alpha source term model of the leaf vortex generator, and realizes the optimal design of the leaf vortex generator based on the model. Compared with the existing source term model of the leaf vortex generator, the BAYC-alpha source term model has higher accuracy under large attack angle conditions, and the leaf vortex generator designed by using the model has higher reliability in the field of computational fluid dynamics.

Description

A Design Method for Blade-Type Vortex Generators Based on the BAYC-α Source Term Model Technical Field

[0001] This invention relates to a design method for a bladed vortex generator based on the BAYC-α source term model, belonging to the field of aircraft design and flow control technology. Background Technology

[0002] For a long time, generating the mesh around a bladed vortex generator has been a challenging task in its design due to the significant size difference between the generator and the controlled object. Therefore, in computational fluid dynamics simulations, using simplified models to replace the bladed vortex generator has become an important research direction.

[0003] Currently, technicians have developed various simplified models for bladed vortex generators, among which the BAYC model is relatively successful. However, the BAYC source term model has low accuracy in predicting the performance of bladed vortex generators under high angle-of-attack conditions. Therefore, optimizing the design of bladed vortex generators by improving the BAYC source term model has significant practical implications. Summary of the Invention

[0004] The purpose of this invention is to address the problems and defects of existing technologies, and to solve the technical problem of low prediction accuracy of the BAYC source term model under high angle of attack conditions in the actual design process of bladed vortex generators. This invention creatively proposes a design method for bladed vortex generators based on the BAYC-α source term model.

[0005] This method improves the BAYC source term model, forming a new BAYC-α source term model for the bladed vortex generator, and realizes the optimized design of the bladed vortex generator based on this model.

[0006] Beneficial effects

[0007] The method of the present invention has the following advantages compared with the prior art:

[0008] 1. Compared with existing blade vortex generator source term models, the BAYC-α source term model proposed in this invention has higher accuracy under high angle of attack conditions.

[0009] 2. The design results obtained using the method of this invention have higher reliability in computational fluid dynamics. Attached Figure Description

[0010] Figure 1 is a schematic diagram of the vector direction in the eddy current generator;

[0011] Figure 2 is a schematic diagram of the force situation of the bladed vortex generator at a large angle of attack;

[0012] Figure 3 is a schematic diagram of the basic process of the optimization design method for bladed vortex generators.

[0013] Figure 4 is a flowchart of the optimization design method for a bladed vortex generator.

[0014] Figure 5 is a schematic diagram of the selection range for the position of the bladed vortex generator in the blade cascade of the NACA65 prototype.

[0015] Figure 6 shows the limit streamline diagram of the suction surface of the NACA65 prototype blade cascade.

[0016] Figure 7 shows the suction surface limit streamline diagram of the NACA65 blade cascade after applying a vortex generator according to this method. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0018] The present invention is achieved using the following technical solution.

[0019] A design method for bladed vortex generators based on the BAYC-α source term model.

[0020] First, we introduce the existing source term construction method for vortex generator models. The vector directions in the lift model of a bladed vortex generator are shown in Figure 1, where... For the spanwise direction of the eddy current generator, The chord direction and vector of the eddy current generator. for σ is the installation angle of the eddy current generator, and α is the angle of attack of the eddy current generator. For lift The unit vector, perpendicular to the velocity vector. and span vector For resistance The unit vector, and the velocity vector The directions are opposite. Based on the small angle assumption:

[0021]

[0022] in, π is the unit vector in the direction of velocity; π is the mathematical constant pi.

[0023] Based on the above definition, the eddy current generator source term model S VG The source term is represented as:

[0024]

[0025] Among them, F Lx F Ly and FLz Lift The components in the x, y, and z axes, F Dx F Dy and F Dz Resistance Components in the x, y, and z axes.

[0026] Lift in the BAYC model and resistance They are respectively:

[0027]

[0028]

[0029] Among them, C L C D These are the lift coefficient and drag coefficient, respectively. C can typically be expressed as... L Set it to 5 or 10, and set C D Set to 5 or 10. u, v, and w are the velocity components in the x, y, and z axes, respectively, and J is the Jacobian matrix. For the incoming flow pressure, A VG V represents the lateral surface area of ​​the eddy current generator. i ∑V is the volume of the i-th grid occupied by the eddy current generator. i This is the total volume of the mesh to which the BAYC model is applied. (Replace the unit vector with the angle of attack in Equation 3:)

[0030]

[0031] Expanding lift and drag in a Cartesian coordinate system and substituting them into Equation 2 yields the matrix form of the source terms of the BAYC model.

[0032] Then, the BAYC-α source term model of the bladed vortex generator is constructed. The details are as follows:

[0033] Figure 2 shows the force situation of the bladed vortex generator at a high angle of attack. The direction of its aerodynamic resultant force N is perpendicular to the vortex generator, and it can be further decomposed into a drag component N. D and lift component N L .

[0034] The aerodynamic force N(α) acting on the vortex generator is related to the relative oncoming area sinα of the vortex generator. The magnitude of the aerodynamic force N(α) increases with the increase of the relative oncoming area, and the functional relationship between the aerodynamic force N(α) and the relative oncoming area is as follows:

[0035] N(α)=a sinb α (5)

[0036] Where a and b are both undetermined coefficients greater than 0. If the values ​​of a and b are determined, the aerodynamic force is given by Equation 1.

[0037] When α = π / 2, we get from equation 5:

[0038]

[0039] The pressure drag acting on the flat plate perpendicular to the flow direction is:

[0040]

[0041] Among them, C π / 2 ρ is the pressure drag coefficient of the two-dimensional plate perpendicular to the flow direction, and its value can be taken as 2; ρ is the fluid density; U is the incoming flow velocity at infinity; C is the length of the vortex generator; α is the angle of attack of the vortex generator.

[0042] Comparing it with Equation 2, we get:

[0043] a=ρU 2 C (8)

[0044] Given the incoming flow conditions and geometric parameters, it is clear that 'a' is a constant. Substituting Equation 8 into Equation 5, the aerodynamic force N(α) acting on the vortex generator and the aerodynamic coefficient C... N They are respectively:

[0045] N(α)=ρU 2 Csin b α (9)

[0046] C N =2sin b α (10)

[0047] Therefore, the lift component coefficient N of the aerodynamic force L (α) and lift component coefficient C NL (α) are respectively:

[0048] N L (α)=N cosα=ρU 2 C sin b αcosα (11)

[0049] C NL (α)=2sin b αcosα (12)

[0050] Because of the symmetry of the bladed vortex generator, when the angle of attack is at α and -α, the magnitudes of the lift component coefficients must be equal and the directions opposite, that is:

[0051] C NL (α)=-C NL (-α) (13)

[0052] Therefore, parameter b is an odd number, and based on the comparison of experimental results, b = 1.

[0053] The pressure coefficient and lift / drag coefficient under high angle of attack (i.e., angle of attack greater than 20°) are respectively:

[0054] C N =2sinα, C NL =sin2α,C ND =2sin 2 α (14)

[0055] In summary, the BAYC-α eddy current generator model is as follows:

[0056]

[0057] The design of a bladed vortex generator using the above model includes the following steps:

[0058] Step 1: Generate a mesh without a vortex generator based on the prototype geometry of the installation object (such as an air wing, air intake, etc.) that generates the blade vortex generator;

[0059] Step 2: Define the design parameter range for the bladed vortex generator;

[0060] Step 3: Sample within the design parameter range of the bladed vortex generator to form a sample library of multiple bladed vortex generator design schemes;

[0061] Step 4: Based on the BAYC-α source term model, perform computational fluid dynamics simulations on all generated bladed vortex generator scheme samples to obtain the optimization objective function results for each sample;

[0062] Step 5: Construct a response surface or machine learning model (such as a support vector machine model) based on the design parameters of the blade vortex generator and the objective function results of each sample;

[0063] Step 6: Use global optimization methods (such as genetic algorithms, simulated annealing algorithms, etc.) to optimize the constructed response surface or machine learning model;

[0064] Furthermore, based on the optimization results of the bladed vortex generator design scheme, a realistic mesh was established for computational fluid dynamics simulation to verify the optimization results. If the design requirements are not met, supplementary sampling is performed around the optimization results and the above process is repeated to obtain more accurate optimization results.

[0065] Step 7: Based on the final optimization results, design the blade vortex generator.

[0066] Example

[0067] This example describes a specific implementation scheme for designing an end-zone vortex generator using this method for a Naca 65 inline cascade.

[0068] Figure 3 is a schematic diagram of the basic process of the bladed vortex generator optimization design method; Figure 4 is a flowchart of the bladed vortex generator optimization design method; Figure 5 is a schematic diagram of the selection range of the bladed vortex generator position in the NACA65 prototype blade cascade; Figure 6 is a diagram of the limit streamline of the suction surface of the NACA65 prototype blade cascade; Figure 7 is a diagram of the limit streamline of the suction surface of the NACA65 blade cascade after the vortex generator is applied according to the design method described in this patent; based on this, the specific implementation of the present invention in a specific application scenario is described.

[0069] The NACA65 prototype blade cascade exhibits corner separation dominated by transverse secondary flow. This cascade has a chord length of 128.23 mm, a consistency of 1.6, an airfoil bend of 42°, and an installation angle of 11°. To suppress the transverse secondary flow, a vortex generator was designed within it in this example.

[0070] First, the VG design parameters are sampled, as shown in Figure 3. In this process, the selected position of the VG on the endwall is first parameterized, as shown in Figure 5; then, the MATLAB toolbox is used to perform Latin hypercube sampling on the VG's length, height, angle of attack, and position parameters, as shown in Figure 4.

[0071] Secondly, CFD numerical simulations were performed on the NACA65 cascade with each VG sample based on the BAYC-α source term model, and the single / multi-objective functions such as the total pressure loss coefficient and separation height were statistically analyzed, as shown in Figure 3.

[0072] Then, a global optimization algorithm (such as constructing a response surface based on a Kriging model and then using a genetic algorithm to find the optimal solution) is used to perform optimization based on statistical results, and the optimization results including VG height, length, angle of attack, and position parameters are obtained, as shown in Figures 3 and 4.

[0073] Subsequently, the optimization results were modeled using a real mesh and CFD numerical simulations were performed to verify the optimization design results, as shown in Figures 3 and 4.

[0074] Finally, check the verification results. If they do not meet the design requirements, supplementary sampling can be performed around the optimized results, and the above optimization process can be repeated to obtain more accurate optimization results. The effect of suppressing corner separation achieved by the VG optimization design in the NACA65 in-line cascade is shown in Figures 6 and 7.

Claims

1. A design method for a bladed vortex generator based on the BAYC-α source term model, characterized in that, The steps include: Step 1: Generate a mesh without a vortex generator based on the prototype geometry of the installation object with a blade vortex generator; Step 2: Define the design parameter range for the bladed vortex generator; Step 3: Sample within the design parameter range of the bladed vortex generator to form a sample library of multiple bladed vortex generator design schemes; Step 4: Based on the BAYC-α source term model, perform computational fluid dynamics simulations on all generated bladed vortex generator scheme samples to obtain the optimization objective function results for each sample; The BAYC-α source term model is as follows: in, Indicates lift. Indicates resistance; C L C D These are the lift coefficient and drag coefficient, respectively; A VG V is the lateral surface area of ​​the eddy current generator; i ΣV is the volume of the i-th grid occupied by the eddy current generator. i ρ is the total volume of the mesh to which the BAYC model is applied; ρ is the fluid density; U is the incoming flow velocity at infinity. For the spanwise direction of the eddy current generator, The chord direction and vector of the eddy current generator. for σ is the installation angle of the eddy current generator, and α is the angle of attack of the eddy current generator; For lift The unit vector, perpendicular to the velocity vector. and span vector For resistance The unit vector, and the velocity vector In the opposite direction; Step 5: Based on the design parameters and objective function results of the bladed vortex generator for each sample, construct a response surface or machine learning model; Step 6: Use a global optimization method to optimize the constructed response surface or machine learning model; For the optimization results, establish a real mesh to perform computational fluid dynamics simulation to verify the optimization results; If the design requirements are not met, perform supplementary sampling around the optimization results and repeat the above process to obtain more accurate optimization results; Step 7: Design the bladed vortex generator according to the final optimization results.

2. The design method for a bladed vortex generator based on the BAYC-α source term model as described in claim 1, characterized in that, C L Set it to 5.

3. The design method for a bladed vortex generator based on the BAYC-α source term model as described in claim 1, characterized in that, C L Set it to 10.

4. The design method for a bladed vortex generator based on the BAYC-α source term model as described in claim 1, characterized in that, C D Set it to 5.

5. The design method for a bladed vortex generator based on the BAYC-α source term model as described in claim 1, characterized in that, C D Set it to 10.

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