A modeling and uncertainty quantification framework for a flexible structure with macrofiber composite actuators operating in hysteretic regimes

被引:14
作者
Hu, Zhengzheng [1 ]
Smith, Ralph C. [1 ]
Burch, Nathanial [2 ]
Hays, Michael [3 ]
Oates, William S. [3 ]
机构
[1] N Carolina State Univ, Dept Math, Ctr Res Sci Computat, Raleigh, NC 27695 USA
[2] N Carolina State Univ, SAMSI, Dept Math, Raleigh, NC 27695 USA
[3] Florida State Univ, Dept Mech Engn, Tallahassee, FL 32306 USA
关键词
Active composites; morphing; piezoelectric; optimization; hysteresis; HOMOGENIZED ENERGY-MODEL; CHARACTERIZING POLARIZATION; STRAINS;
D O I
10.1177/1045389X13489781
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Macrofiber composites are low cost, durable, and flexible piezoceramic devices that are presently being considered for applications that include shape control of airfoils for improved flight performance, vibration, and noise suppression and energy harvesting. However, macrofiber composites also exhibit hysteresis and constitutive nonlinearities that need to be incorporated in models and model-based control designs to achieve their full capability. In this article, we combine constitutive relations, constructed using the homogenized energy model for ferroelectric hysteresis, with Euler-Bernoulli theory to construct a dynamic macrofiber composite model that quantifies a range of rate-dependent hysteretic behavior of macrofiber composites. Using homogenizing strategies, the macrofiber composite patch is treated as a monolithic material with effective parameters. We initially calibrate the model by estimating parameters through a least squares fit to a subset of the measured data. We find that the estimated parameters yield very accurate fits for quasi-static hysteresis. The estimated parameters also provide reasonably accurate predictions for a range of frequencies that include the first two harmonics. Second, we employ an adaptive Markov chain Monte Carlo algorithm to construct densities and analyze the correlation between parameters. The kernel density estimates derived from the Markov chain Monte Carlo chains imply that most of the model parameters exhibit non-Gaussian distributions.
引用
收藏
页码:204 / 228
页数:25
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