Identification of nonlinear parameters for reduced order models

被引:26
|
作者
Spottswood, S. M.
Allemang, R. J.
机构
[1] USAF, Res Lab, Wright Patterson AFB, OH 45433 USA
[2] Univ Cincinnati, Dept Mech Ind & Nucl Engn, Struct Dynam Res Lab, Cincinnati, OH 45221 USA
关键词
D O I
10.1016/j.jsv.2006.01.009
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Constructing nonlinear structural dynamic models is a goal for numerous research and development organizations. Such a predictive capability is required in the development of advanced, high-performance aircraft structures. Specifically, the ability to predict the response of complex structures to engine induced and aero acoustic loading has long been a United States Air Force (USAF) goal. Sonic fatigue has plagued the USAF since the advent and adoption of the turbine engine. While the problem has historically been a maintenance one, predicting the dynamic response is crucial for future aerospace vehicles. Decades have been spent investigating the dynamic response and untimely failure of aircraft structures, yet little work has been accomplished towards developing practical nonlinear prediction methods. Further, the last decade has witnessed an appreciable amount of work in the area of nonlinear parameter identification. This paper outlines a unique and important extension of a recently introduced nonlinear identification method: Nonlinear Identification through Feedback of the Outputs (NIFO). The novel extension allows for a ready means of identifying nonlinear parameters in reduced order space using experimental data. The nonlinear parameters are then used in the assembly of reduced order models, thus providing researchers with a means of conducting predictive studies prior to expensive and questionable experimental efforts. This paper details both an analytical and experimental study conducted on a well-characterized clamped-clamped beam subjected to broadband random loading. Amplitude dependent, constant stiffness parameters were successfully identified for a multiple-degree-of-freedom (MDOF) nonlinear reduced order model. The nonlinear coefficients identified from the analytical scenario compare well with previously published studies of the beam. Nonlinear parameters were also successfully identified from the raw experimental data. Finally, a MDOF nonlinear reduced order model, constructed from experimental data, was used to predict the experimental response of the beam to other loading conditions. Beam response spectra and average displacement values from the prediction model also compare well with the experimental results. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:226 / 245
页数:20
相关论文
共 50 条
  • [21] Stochastic change detection in uncertain nonlinear systems using reduced-order models: system identification
    Yun, Hae-Bum
    Masri, Sami F.
    [J]. SMART MATERIALS AND STRUCTURES, 2008, 17 (01)
  • [22] Evaluation of Geometrically Nonlinear Reduced-Order Models with Nonlinear Normal Modes
    Kuether, Robert J.
    Deaner, Brandon J.
    Hollkamp, Joseph J.
    Allen, Matthew S.
    [J]. AIAA JOURNAL, 2015, 53 (11) : 3273 - 3285
  • [23] Considerations for Indirect Parameter Estimation in Nonlinear Reduced Order Models
    Guerin, Lorraine C. M.
    Kuether, Robert J.
    Allen, Matthew S.
    [J]. NONLINEAR DYNAMICS, VOL 1, 2017, : 327 - 342
  • [24] Layered reduced-order models for nonlinear aerodynamics and aeroelasticity
    Kou, Jiaqing
    Zhang, Weiwei
    [J]. JOURNAL OF FLUIDS AND STRUCTURES, 2017, 68 : 174 - 193
  • [25] Families of reduced order models that achieve nonlinear moment matching
    Ionescu, T. C.
    Astolfi, A.
    [J]. 2013 AMERICAN CONTROL CONFERENCE (ACC), 2013, : 5518 - 5523
  • [26] Nonlinear Behavior and Reduced-Order Models of Islanded Microgrid
    Yang, Jingxi
    Tse, Chi K.
    Liu, Dong
    [J]. IEEE TRANSACTIONS ON POWER ELECTRONICS, 2022, 37 (08) : 9212 - 9225
  • [27] REDUCED ORDER MODELING FOR NONLINEAR MULTI-COMPONENT MODELS
    Abdel-Khalik, Hany S.
    Bang, Youngsuk
    Kennedy, Christopher
    Hite, Jason
    [J]. INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2012, 2 (04) : 341 - 361
  • [28] Automated identification and calculation of the parameters of nonlinear material models
    Mohrmann, R
    [J]. IUTAM SYMPOSIUM ON FIELD ANALYSES FOR DETERMINATION OF MATERIALS PARAMETERS - EXPERIMENTAL AND NUMERICAL ASPECTS, 2003, 109 : 113 - 122
  • [29] Identification and construction of reduced order models for infinite-dimensional systems in nonlinear elastodynamics - Proper orthogonal decompositions
    Georgiou, IT
    [J]. IUTAM Symposium on Chaotic Dynamics and Control of Systems and Processes in Mechanics, 2005, 122 : 203 - 212
  • [30] Hierarchical deep learning for data-driven identification of reduced-order models of nonlinear dynamical systems
    Li, Shanwu
    Yang, Yongchao
    [J]. NONLINEAR DYNAMICS, 2021, 105 (04) : 3409 - 3422