Unsteady aerodynamic modeling based on fuzzy scalar radial basis function neural networks

科研成果: 期刊稿件文章同行评审

31 引用 (Scopus)

摘要

In this paper, a fuzzy scalar radial basis function neural network is proposed, in order to overcome the limitation of traditional aerodynamic reduced-order models having difficulty in adapting to input variables with different orders of magnitude. This network is a combination of fuzzy rules and standard radial basis function neural network, and all the basis functions are defined as scalar basis functions. The use of scalar basis function will increase the flexibility of the model, thus enhancing the generalization capability on complex dynamic behaviors. Particle swarm optimization algorithm is used to find the optimal width of the scalar basis function. The constructed reduced-order models are used to model the unsteady aerodynamics of an airfoil in transonic flow. Results indicate that the proposed reduced-order models can capture the dynamic characteristics of lift coefficients at different reduced frequencies and amplitudes very accurately. Compared with the conventional reduced-order model based on recursive radial basis function neural network, the fuzzy scalar radial basis function neural network shows better generalization capability for different test cases with multiple normalization methods.

源语言英语
页(从-至)5107-5121
页数15
期刊Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
233
14
DOI
出版状态已出版 - 1 11月 2019

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