{"title":"A data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings","authors":"Yuanhang Zhu , Kenneth Breuer","doi":"10.1016/j.jfluidstructs.2025.104282","DOIUrl":null,"url":null,"abstract":"<div><div>We propose and validate a data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings. We first train a neural networks regression model for the nonlinear aerodynamic moment using data obtained from experimental measurements during prescribed pitching oscillations and at fixed angles of attack. We then embed this model into an ordinary differential equation solver to solve the governing equation of the passive aeroelastic system with desired structural parameters. The system dynamics predicted by the proposed data-driven approach are characterized and compared with those obtained from physical experiments. The predicted and experimental pitching amplitude, frequency and aerodynamic moment responses are found to be in excellent agreement. Both the inertia-dominated mode and the hydrodynamic-dominated mode are successfully predicted. The transient growth and saturation of the pitching oscillation amplitude and the aerodynamic moment are also faithfully captured by the proposed approach. Additional test cases demonstrate the broad applicability and good scalability potential of this approach.</div></div>","PeriodicalId":54834,"journal":{"name":"Journal of Fluids and Structures","volume":"134 ","pages":"Article 104282"},"PeriodicalIF":3.4000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Fluids and Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889974625000179","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
We propose and validate a data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings. We first train a neural networks regression model for the nonlinear aerodynamic moment using data obtained from experimental measurements during prescribed pitching oscillations and at fixed angles of attack. We then embed this model into an ordinary differential equation solver to solve the governing equation of the passive aeroelastic system with desired structural parameters. The system dynamics predicted by the proposed data-driven approach are characterized and compared with those obtained from physical experiments. The predicted and experimental pitching amplitude, frequency and aerodynamic moment responses are found to be in excellent agreement. Both the inertia-dominated mode and the hydrodynamic-dominated mode are successfully predicted. The transient growth and saturation of the pitching oscillation amplitude and the aerodynamic moment are also faithfully captured by the proposed approach. Additional test cases demonstrate the broad applicability and good scalability potential of this approach.
期刊介绍:
The Journal of Fluids and Structures serves as a focal point and a forum for the exchange of ideas, for the many kinds of specialists and practitioners concerned with fluid–structure interactions and the dynamics of systems related thereto, in any field. One of its aims is to foster the cross–fertilization of ideas, methods and techniques in the various disciplines involved.
The journal publishes papers that present original and significant contributions on all aspects of the mechanical interactions between fluids and solids, regardless of scale.