Skip to main content

ORIGINAL RESEARCH article

Front. Phys.
Sec. Interdisciplinary Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1452876
This article is part of the Research Topic Wave Propagation in Complex Environments, Volume II View all articles

Research on the prediction method of wing structure noise based on the combination of conditional generative adversarial neural network and numerical methods

Provisionally accepted
Shujie Jiang Shujie Jiang 1*Yuxiang Liang Yuxiang Liang 1*Yu Cheng Yu Cheng 2Lingyu Gao Lingyu Gao 3
  • 1 State Key Laboratory of Aerodynamics, Mianyang, Sichuan 621000, P.R. China, Sichuan, China
  • 2 Department of Neurosurgery, Zhumadian Central Hospital, Zhumadian, China
  • 3 School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

The final, formatted version of the article will be published soon.

    This paper presents a technique for predicting noise generated by airfoil structures that combines deep learning techniques with traditional numerical methods. In traditional numerical methods, accurately predicting the noise of airfoil structures requires significant computational resources, making it challenging to perform low-noise optimization design for these structures. To expedite the prediction process, this study introduces Conditional Generative Adversarial Networks (CGAN).By replacing the generator and discriminator of CGAN with traditional regression neural network models, the suitability of CGAN for regression prediction is ensured. In this study, the data computation was accelerated by expanding the kernel function in the traditional boundary element method using a Taylor series. Based on the resulting data, an alternative predictive model for wing structure noise was developed by integrating Conditional Generative Adversarial Networks (CGAN).Finally, the effectiveness and feasibility of the proposed method are demonstrated through three case studies.

    Keywords: Airfoil structure, CGAN, Boundary element, Aeroacoustic noise, computational fluid dynamics

    Received: 21 Jun 2024; Accepted: 04 Sep 2024.

    Copyright: © 2024 Jiang, Liang, Cheng and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Shujie Jiang, State Key Laboratory of Aerodynamics, Mianyang, Sichuan 621000, P.R. China, Sichuan, China
    Yuxiang Liang, State Key Laboratory of Aerodynamics, Mianyang, Sichuan 621000, P.R. China, Sichuan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.