Characteristic nonlinear system identification of local attachments with clearance nonlinearities
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ORIGINAL PAPER
Characteristic nonlinear system identification of local attachments with clearance nonlinearities Aryan Singh . Keegan J. Moore
Received: 1 February 2020 / Accepted: 3 October 2020 Ó Springer Nature B.V. 2020
Abstract This research considers the identification of mathematical models for the dynamics of local nonlinear attachments with clearance nonlinearities directly from measured transient-response data. Specifically, a recently developed method, designated as the characteristic nonlinear system identification (CNSI) method, is implemented to identify the dynamics of local nonlinear attachments with clearance nonlinearities. The first major advantage of the CNSI procedure is that it provides the means to correlate the equation of motion governing the attachment directly to the instantaneous frequency and damping observed experimentally. The second major advantage is that it identifies the dynamics of the attachment entirely from mass measurements and the measured transient response of the attachment and its installation points. The first phase of the CNSI approach focuses on extracting the characteristic displacement, characteristic velocity, instantaneous frequency, and instantaneous damping from the measured response. The second phase begins with the analyst proposing a model for the dynamics based on the instantaneous frequency and damping curves, and the unknown parameters of the model are then systematically identified through a curve-fitting
A. Singh K. J. Moore (&) Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA e-mail: [email protected]
procedure. The CNSI method is demonstrated experimentally using the response of a linear model airplane wing with a local nonlinear attachment equipped with a clearance nonlinearity. Keywords System identification Nonlinear dynamics Local attachment Nonsmooth nonlinearity Nonlinear energy sink Nonlinear system identification Data-driven modeling Datadriven identification
1 Introduction System identification concerns the creation of a mathematical model for a system from experimentally measured response data, and it is often employed to improve an existing model when that model fails to reproduce measurements. System identification for linear vibrating structures has been investigated since the advent of modern computers and has evolved from laboratory experiments to wide-spread use in industry [1]. Of particular note is the widely successful experimental modal analysis based on Fourier transform theory, which is well documented in [2, 3]. However, in practice, many structures exhibit substantial nonlinearities that introduce multi-physical interactions into the dynamics including nonstationary behavior, bifurcations, internal resonances and resonance capture phenomena, and quasi-periodic and
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chaotic responses (among many other phenomena) [4–6]. Methods based on Fourier transforms fail to capture the appear
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