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Artificial-neural-network-based inverse design method for aircraft airfoils/wings

An artificial neural network and anti-design technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as different intelligent algorithms and unsatisfactory results, and achieve accurate results

Active Publication Date: 2015-08-12
FUDAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the complexity of aircraft component design problems, the intelligent algorithms adopted are not the same, and the results obtained are not satisfactory.

Method used

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  • Artificial-neural-network-based inverse design method for aircraft airfoils/wings
  • Artificial-neural-network-based inverse design method for aircraft airfoils/wings
  • Artificial-neural-network-based inverse design method for aircraft airfoils/wings

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Experimental program
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Embodiment 1

[0031] Design requirements: Under a cruise Mach number Ma=0.785, the design state is an airfoil with the following parameters:

[0032] Design Mach number Ma=0.71

[0033]Design angle of attack á=2.53

[0034] Reynolds number Re=23,000,000

[0035] First create the airfoil database. In terms of shape structure, the airfoil is relatively simple. In this example, the airfoil database contains 208 reconstructed airfoils. Each airfoil in the database must have good aerodynamic performance; and the geometric configuration of the airfoil must have as large a span as possible, that is, there must be obvious shape differences between the airfoils. Only in this way can the efficiency and integrity of the airfoil database be guaranteed. Table 1 and Table 2 summarize the span of airfoil movement parameters and shape parameters, respectively.

[0036] Table 1 Summary of airfoil aerodynamic parameters

[0037]

[0038] Table 2 Summary of airfoil shape parameters

[0039] ...

Embodiment 2

[0047] Design requirements: The designed lift-to-drag ratio is 25.8, the lift coefficient is 0.5, the form drag coefficient is 0.0105, the induced drag coefficient is 0.009, the shock wave drag coefficient is 0.00017, and the moment coefficient is 0.23; the wing lift coefficient is 0.43, and the drag coefficient is Wing profile with a moment coefficient of 0.0095 and -0.13.

[0048] The main aerodynamic parameters (wing lift-to-drag ratio and lift coefficient) are proposed by the designer, and the rest of the secondary aerodynamic parameters are given by interpolation method with reference to the main aerodynamic parameters and the 214 sets of aerodynamic input ranges in the database, as shown in Table 5.

[0049] Table 5 Summary of wing aerodynamic parameters

[0050] .

[0051] First use the SOM neural network to classify the wing data in the database according to the aerodynamic performance, take out a set of data corresponding to the target aerodynamic input, and select...

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Abstract

The invention belongs to the technical field of design of aircrafts and in particular relates to an artificial-neural-network-based inverse design method for aircraft airfoils / wings. The method comprises the following steps: rebuilding expression modes of airfoils / wings by using PARSEC parameterization method of airfoils / wings, and implementing inverse design technology by using an artificial neural network (ANN) algorithm. By virtue of the artificial-neural-network-based inverse design method for aircraft airfoils / wings, a conventional cut-and-try method with complex design and low efficiency for aircraft airfoils / wings is put aside; a relation between the aerodynamic performance of airfoils / wings and the geometric profile of aircraft airfoils / wings is directly built; the artificial-neural-network-based parameterization inverse design for aircraft airfoils / wings is implemented. The artificial-neural-network-based inverse design method for aircraft airfoils / wings is high in speed and is highly suitable for the overall design of aircrafts, especially the initial design; the artificial neural network algorithm is in place, so that the generated results are highly accurate.

Description

technical field [0001] The invention belongs to the technical field of aircraft design, and in particular relates to an aircraft airfoil / wing inverse design method. Background technique [0002] The inverse design method in aircraft component design, that is, a kind of "what you need is what you get", can directly obtain the geometric data of the aircraft component that meets the requirements according to the given aerodynamic performance requirements. At present, the usual method of aircraft airfoil / wing design is the traditional, cumbersome and inefficient enumeration-iterative method (cut-and-try). performance, feed it back to make new changes to the geometric data (often such changes are not very closely related to aerodynamic performance, and show greater randomness), and then perform new aerodynamic performance calculations, and so on and so forth Until a new airfoil / wing is obtained that meets the required aerodynamic performance. Due to the lack of pertinence of su...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
Inventor 孙刚王舒悦孙燕杰陶俊
Owner FUDAN UNIV
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