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首页> 外文期刊>Journal of intelligent material systems and structures >The homogenized energy model for characterizing polarization and strains in hysteretic ferroelectric materials: Implementation algorithms and data-driven parameter estimation techniques
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The homogenized energy model for characterizing polarization and strains in hysteretic ferroelectric materials: Implementation algorithms and data-driven parameter estimation techniques

机译：表征滞后铁电材料中极化和应变的均匀能量模型：实现算法和数据驱动的参数估计技术

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摘要

Ferroelectric materials, such as lead zirconate titanate, lanthanum-doped lead zirconate titanate, lead manganese niobate, and BaTiO_3, provide unique actuator and sensor capabilities for applications including nanopositioning, high-speed valves and fuel injectors, camera focusing and shutter mechanisms, ultrasonic devices for biomedical imaging and treatment, and energy harvesting devices. However, to achieve the full potential of the materials, it is necessary to develop and employ models that quantify the creep, rate-dependent hysteresis, and constitutive nonlinearities that are intrinsic to the materials due to their domain structure. The success of models requires that they be highly efficient to implement since real-time applications can require kilo hertz to mega hertz rates. The calibration of models for specific materials, devices, and applications requires efficient and robust parameter estimation algorithms. Finally, control designs can be facilitated by models that admit efficient and robust approximate inversion. The homogenized energy model is a multiscale, micro-mechanical framework that quantifies a range of hysteretic phenomena intrinsic to ferroelectric, ferromagnetic, and fer-roelastic materials. In this article, we present highly efficient implementation and parameter estimation algorithms for the ferroelectric model. This includes techniques to construct analytic Jacobians and data-driven algorithms to determine initial parameter estimates to facilitate subsequent optimization. The efficiency of these algorithms facilitates material and device characterization and provides the basis for constructing efficient and robust inverse algorithms for model-based control design. The model implementation, calibration, and validation are illustrated using rate-dependent lead zirconate titanate data and single-crystal BaTiO_3 data.
机译：铁电材料，例如锆钛酸铅，镧掺杂锆钛酸铅，铌酸锰铅和BaTiO_3，为纳米定位，高速阀和燃料喷射器，相机聚焦和快门机构，超声设备等应用提供了独特的执行器和传感器功能。用于生物医学成像和治疗以及能量收集装置。但是，为了充分发挥材料的潜力，有必要开发和采用能够量化由于材料的畴结构而固有的蠕变，速率相关的磁滞和本构非线性的模型。模型的成功需要高效地实施，因为实时应用可能需要几千赫兹到几兆赫兹的速率。针对特定材料，设备和应用的模型校准需要高效且强大的参数估计算法。最后，可以通过允许有效和鲁棒的近似反演的模型来简化控制设计。均质化的能量模型是一个多尺度的微机械框架，可量化铁电，铁磁和铁弹性材料固有的一系列磁滞现象。在本文中，我们提出了铁电模型的高效实现和参数估计算法。这包括构造解析雅可比行列式的技术和数据驱动算法，以确定初始参数估计值，以利于后续优化。这些算法的效率有助于材料和设备的表征，并为构建基于模型的控制设计的高效而强大的逆算法提供了基础。使用速率相关的锆钛酸铅钛酸盐数据和单晶BaTiO_3数据说明了模型的实现，校准和验证。

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