Observation vector reconstruction-based nonparametric nonlinear restoring force identification for granules-structures coupled vibrating system

IF 2.3 3区 工程技术 Q2 ACOUSTICS Journal of Vibration and Control Pub Date : 2024-07-20 DOI:10.1177/10775463241260898
Jinlu Dong, Jian Li, Guangyang Hong, Hang Li, Yang Ning
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Abstract

The nonlinear restoring force (NRF) generated by the collision and friction between particles and structures is the leading cause of the complex dynamic response of the granules-structures coupled vibrating system (GSCVS). Identification of NRF can provide critical information for post-event damage diagnosis and structural design of immersed structures. However, the spatial distribution and dynamic response of the particles near the structures are diverse and complex, making it difficult to describe the NRF with an accurate mathematical model. This paper proposed a data-based nonparametric method to estimate the NRF in the GSCVS. A nonparametric model of NRF that considered the additional effects of particles on both sides of the structures and consisted of system response and undetermined coefficients was developed. The observation vector of the conventional Extended Kalman Filter (EKF) was reconstructed by the sparse measurement of the strain response. The reconstructed observation vector contains three response components: translational displacement, translational acceleration, and rotational acceleration, in which the rotational acceleration response is difficult to measure in engineering applications. The proposed EKF based on observation vector reconstruction (EKF-OVR) can identify the undetermined coefficients in the nonparametric model, and then the NRF can be calculated. Numerical studies showed that EKF-OVR achieved higher accuracy and noise robustness than the conventional EKF and the data fusion based EKF. A dynamic experimental study on granules-beam coupled vibrating system (GBCVS) was conducted, and the proposed algorithm was employed to identify the NRF of the GBCVS. The effects of excitation amplitude, particle size, and immersed depth on NRF are analyzed, and it is found that higher harmonic components in the NRF led to period doubling and chaos of the beam response.
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基于观测矢量重构的颗粒-结构耦合振动系统非参数非线性恢复力识别
颗粒与结构之间的碰撞和摩擦产生的非线性恢复力(NRF)是颗粒-结构耦合振动系统(GSCVS)产生复杂动态响应的主要原因。NRF 的识别可为浸入式结构的事后损坏诊断和结构设计提供关键信息。然而,结构附近颗粒的空间分布和动态响应多样而复杂,因此很难用精确的数学模型来描述 NRF。本文提出了一种基于数据的非参数方法来估计 GSCVS 中的 NRF。非参数 NRF 模型考虑了结构两侧颗粒的额外影响,由系统响应和未确定系数组成。通过对应变响应的稀疏测量,重建了传统扩展卡尔曼滤波器(EKF)的观测向量。重建的观测向量包含三个响应分量:平移位移、平移加速度和旋转加速度,其中旋转加速度响应在工程应用中很难测量。所提出的基于观测矢量重构的 EKF(EKF-OVR)可以识别非参数模型中的未确定系数,然后计算 NRF。数值研究表明,与传统的 EKF 和基于数据融合的 EKF 相比,EKF-OVR 具有更高的精度和噪声鲁棒性。对颗粒-横梁耦合振动系统(GBCVS)进行了动态实验研究,并采用所提出的算法确定了 GBCVS 的 NRF。分析了激励振幅、颗粒大小和浸入深度对 NRF 的影响,发现 NRF 中的高次谐波成分会导致周期倍增和梁响应的混沌。
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来源期刊
Journal of Vibration and Control
Journal of Vibration and Control 工程技术-工程:机械
CiteScore
5.20
自引率
17.90%
发文量
336
审稿时长
6 months
期刊介绍: The Journal of Vibration and Control is a peer-reviewed journal of analytical, computational and experimental studies of vibration phenomena and their control. The scope encompasses all linear and nonlinear vibration phenomena and covers topics such as: vibration and control of structures and machinery, signal analysis, aeroelasticity, neural networks, structural control and acoustics, noise and noise control, waves in solids and fluids and shock waves.
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