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

机译:具有在滞回状态下运行的大纤维复合执行器的柔性结构的建模和不确定性量化框架

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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.
机译:大纤维复合材料是低成本,耐用且柔性的压电陶瓷器件,目前正考虑用于包括翼型的形状控制以改善飞行性能,振动以及噪声抑制和能量收集的应用。但是,大纤维复合材料还具有滞后性和本构非线性,需要将其纳入模型和基于模型的控制设计中,以实现其全部功能。在本文中,我们将使用铁电磁滞的均质化能量模型构造的本构关系与Euler-Bernoulli理论相结合,以构造动态宏观纤维复合材料模型,该模型量化了宏观纤维复合材料的速率相关滞后行为范围。使用均质化策略,将大纤维复合材料贴片视为具有有效参数的整体材料。我们最初通过通过与测量数据的子集拟合的最小二乘法估计参数来校准模型。我们发现估计的参数对准静态磁滞产生非常精确的拟合。估计的参数还可以为包括前两个谐波的频率范围提供合理准确的预测。其次,我们采用自适应马尔可夫链蒙特卡罗算法构造密度并分析参数之间的相关性。从马尔可夫链蒙特卡洛链得出的核密度估计值表明,大多数模型参数均显示非高斯分布。

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