In this paper, we discuss the development and implementation of a 3-D electromechanically coupled homogenized energy model (HEM) for ferroelectric materials. A stochastic-based methodology is introduced and applied to problems involving large scale switching of ferroelectric and ferroelastic materials. Switching criteria for polarization variants are developed using density distributions in three dimensions to accommodate both electrical and mechanical loading and their coupled response. The theory accommodates non-proportional loading and major/minor loop hysteresis. Such formulations are known to accelerate computations for real-time control of nonlinear and hysteretic actuators. The proposed formulation maintains superior computational efficiency in the three dimensional case through the application of density formulations that are based on internal distributions of stress and electric field to produce a distribution of polarization switching events over a range of applied fields and stresses.
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ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
September 18–20, 2017
Snowbird, Utah, USA
Conference Sponsors:
- Aerospace Division
ISBN:
978-0-7918-5826-4
PROCEEDINGS PAPER
A Multi-Axial Electromechanically-Coupled Homogenized Energy Model for Ferroelectric Materials
William S. Oates,
William S. Oates
Florida State University, Tallahassee, FL
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Ralph C. Smith
Ralph C. Smith
North Carolina State University, Raleigh, NC
Search for other works by this author on:
William S. Oates
Florida State University, Tallahassee, FL
Ralph C. Smith
North Carolina State University, Raleigh, NC
Paper No:
SMASIS2017-3848, V002T03A024; 15 pages
Published Online:
November 9, 2017
Citation
Oates, WS, & Smith, RC. "A Multi-Axial Electromechanically-Coupled Homogenized Energy Model for Ferroelectric Materials." Proceedings of the ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 2: Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation; Structural Health Monitoring. Snowbird, Utah, USA. September 18–20, 2017. V002T03A024. ASME. https://doi.org/10.1115/SMASIS2017-3848
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