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Prediction-correction algorithm-based aircraft global pneumatic optimization method

A technology of optimization method and calibration algorithm, which is applied in the direction of instruments, calculations, gene models, etc., can solve the problems of low efficiency and achieve the effect of improving calculation efficiency

Inactive Publication Date: 2016-10-26
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to overcome the inefficiency of the existing aircraft global aerodynamic optimization method, the present invention provides an aircraft global aerodynamic optimization method based on a predictive correction algorithm

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  • Prediction-correction algorithm-based aircraft global pneumatic optimization method
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  • Prediction-correction algorithm-based aircraft global pneumatic optimization method

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[0027] refer to Figure 1-4 . The specific steps of the aircraft global aerodynamic optimization method based on the estimation and correction algorithm of the present invention are as follows:

[0028] The global aerodynamic optimization design of the RAE2822 airfoil, the design state is in the range of transonic small angle of attack, the Mach number is 0.75, the angle of attack is 2.72 degrees, and the Reynolds number is 6.2×10 6 . The flow field solution uses the RANS solver, and the turbulent flow item uses the k-ωSST two-equation model. The CST parameterization method is used to control the geometric shape of the airfoil, and 24 control parameters are selected. The implementation steps are:

[0029] Step 1, select 24 control parameters, and use the CST method to parameterize the RAE2822 airfoil. Perturb the initial CST parameters and select a certain amount of airfoil sample points, 40 in this embodiment. The RBF grid deformation method is used to generate the calc...

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Abstract

The invention discloses a prediction-correction algorithm-based aircraft global pneumatic optimization method, and aims at solving the technical problem that the conventional aircraft global pneumatic optimization methods are low in efficiency. The technical scheme is that the method disclosed in the invention comprises the following steps: firstly carrying out prediction optimization: building an agent model through collecting flow field snapshots, and carrying out preliminary screening on the design space according to a genetic algorithm so as to obtain approximate points of a global optimum point; and carrying out correction optimization: carrying out pneumatic optimizing by utilizing an adjoint gradient and a gradient optimization algorithm to ensure that the predicted approximate points are rapidly converged to the global optimum point. According to the method disclosed in the invention, a flow field order reduction model is constructed by repeatedly using the previously obtained flow field snapshots during the optimization in the step 2, so as to rapidly predict a to-be-calculated flow field, and then correction is carried out on the prediction value by utilizing a full-order flow field solver. Compared with the methods in background technology, the method disclosed in the invention has the effect of improving the calculation efficiency of the flow fields; and for the two-dimensional optimization problem, the calculation of flow field numerical values is reduced from about 200 times in the background technology to about 70 times, so that the optimization effect is remarkable.

Description

technical field [0001] The invention relates to an aircraft global aerodynamic optimization method, in particular to an aircraft global aerodynamic optimization method based on a predictive correction algorithm. Background technique [0002] Global aerodynamic shape optimization design refers to designing the globally optimal aerodynamic geometry shape under given constraints. At present, there are two main categories of mainstream aerodynamic optimization methods: gradient methods and non-gradient methods. The optimization efficiency of the gradient method is high, but it only has local optimization ability. Since the optimization design problems of aerodynamic shape are generally multi-variable and multi-peak, the gradient method generally cannot meet the needs of the global aerodynamic optimization design. Non-gradient methods are generally based on proxy models (such as response surface, Kriging, Gappy POD, etc.) and intelligent algorithms (such as genetic algorithms, s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/12
CPCG06F30/15G06F30/367G06N3/126Y02T90/00
Inventor 李记超蔡晋生屈崑曹长强
Owner NORTHWESTERN POLYTECHNICAL UNIV
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