Aircraft Aerodynamic Optimization Method Based on Data Mining and Genetic Algorithm
A genetic algorithm and data mining technology, applied in the field of aircraft engineering, can solve the problems that the genetic algorithm cannot make full use of prior knowledge and slow convergence speed, achieve high-precision optimization of design parameters, eliminate manual intervention, and improve efficiency
Active Publication Date: 2022-06-03
TSINGHUA UNIV
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Problems solved by technology
[0004] In order to solve the problem that the existing genetic algorithm cannot make full use of prior knowledge in the application of aerodynamic optimization, resulting in slow convergence speed, the present invention proposes an aircraft aerodynamic optimization method based on data mining and genetic algorithm, and the method in the final optimization There is no need for manual intervention in the ongoing stage, and automatic iterative calculations can be realized, which improves the optimization efficiency
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[0019] Embodiments are used to further illustrate the present invention below. The software, file formats and platforms described herein are used to provide further understanding of the present invention, but do not limit the protection scope of the present invention to the scope described in the embodiments.
[0020] Firstly, the missile is selected as the object to optimize the aerodynamic shape of its wing. The optimization goal is to improve the lift-to-drag ratio while keeping the aerodynamic center basically unchanged. The chord length, root chord length and sweep angle of the leading and trailing edges of the airfoil are optimized as design parameters.
[0021] Aiming at the above-mentioned missile aerodynamic optimization problem, the Latin hypercube sampling method is used for sampling in the design space, so that the design parameters of the samples are distributed as evenly as possible in the entire design space, and then the semi-empirical estimation software Missil...
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The invention discloses an aircraft aerodynamic optimization method based on data mining and genetic algorithm, which is used to solve the problem of slow convergence speed when traditional genetic algorithm is used for aerodynamic optimization, and the method does not need manual intervention in the final optimization stage, and can realize automatic optimization. Iterative calculation improves optimization efficiency. The technical solution is to first obtain the design sample library through the method of semi-empirical estimation, then conduct data mining on the sample library, use cluster analysis, variance analysis and decision tree analysis to obtain high-confidence optimization rules, and then use these rules as a priori The knowledge is fused into the genetic algorithm, which is specifically manifested in the dynamic setting of the intersection rules and mutation rules according to the prior rules. Finally, the fused and improved genetic algorithm is used for aerodynamic optimization based on high-precision fluid simulation to obtain excellent design parameters. Compared with the traditional optimization method based on the genetic algorithm, the invention greatly improves the convergence speed, and has great engineering value for the aerodynamic optimization of the aircraft.
Description
technical field [0001] The invention belongs to the technical field of aircraft engineering, in particular to an aircraft aerodynamic optimization method based on data mining and genetic algorithm. Background technique [0002] Aerodynamic optimization refers to the design of the shape and relative positions of the main components of the aircraft, and it is necessary to obtain the design scheme with the optimal aerodynamic performance under the given constraints. Genetic algorithm is a commonly used aerodynamic optimization method. It has good global optimization ability and is suitable for complex multi-extremum optimization problems, but the convergence speed is relatively slow, especially when combined with time-consuming and high-precision fluid simulation for aerodynamic optimization. , this shortcoming is more prominent. [0003] There are many derivative types of genetic algorithm. Compared with the original genetic algorithm, the optimization ability and convergence...
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IPC IPC(8): G06F30/17G06F30/15G06F30/27G06F30/28G06F113/08
CPCG06F30/17G06F30/20Y02T90/00
Inventor 闫星辉朱纪洪匡敏驰王吴凡史恒
Owner TSINGHUA UNIV