Studies of Dipole-Driven Active Materials: Multifield Processing Simulations, Microstructure-Based Constitutive Modeling, and Macroscopic Active Composite Optimization
Open Access
- Author:
- Erol, Anil
- Graduate Program:
- Mechanical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 27, 2019
- Committee Members:
- Paris R Vonlockette, Dissertation Advisor/Co-Advisor
Paris R Vonlockette, Committee Chair/Co-Chair
Mary I Frecker, Committee Member
Zoubeida Ounaies, Committee Member
Francesco Costanzo, Outside Member - Keywords:
- Electro-active polymers
magneto-active elastomers
nonlinear elasticity
smart materials
optimization
modeling
mechanics of materials - Abstract:
- Dipole-based active materials are a wide-ranging class of materials used in industries from aerospace to biomedicine. The advantages of these materials are their ability to be actuated while untethered via electric and/or magnetic fields, and that the interactions of the embedded dipoles can be manipulated on a micro- or even nano-scale. While dipole-based materials are becoming more widely used, there is a lack of rigorous modeling techniques that could help characterize, design, and implement them in applications. This work aims to address the challenges that arise when studying the materials at each length-scale: micro, meso, and macro. At the micro-scale, this work studied the effects of electric and magnetic fields on the alignment and ordering of electromagnetically susceptible particles suspended in an elastomeric fluid medium. While past studies have shown that multiple fields can assemble particles in a fluid in multiple directions, ferrohydrodynamic particle simulations in this study demonstrated that the simultaneous application of electric and magnetic fields on hard magnetic particles with geometric anisotropy can create a hierarchy of structures at different length scales, which can be used to achieve a wider range of structure and properties. The simulation methods included permanent magnetic dipoles and induced electric dipoles, yielding magneto- and dielectro-phoretic effects, and electric and magnetic torques acting against hydrodynamic drag forces. At the meso-scale, the mechanics of a class of dipole-based semicrystalline electro-active polymers (EAPs) called relaxor ferroelectric polymers were modeled to understand how tailored dipole-based microstructures may affect bulk electromechanical response. To fill the gap in EAP modeling literature, this work developed a modeling framework for dipole-based EAPs by utilizing the micromechanics of semicrystalline polymer network model, ascribing dipole-dipole energies to crystalline regions and the eight-chain hyper-elastic model to amorphous regions. Finding the equilibrium of the network model yielded a relationship between the spatial arrangements of dipolar regions and the electromechanical coupling of the bulk material. The findings revealed that the anisotropy of dipole-dipole interactions cause particle arrangements parallel to an external field to generate more electromechanical coupling than all other arrangements, implicating a microstructure that could maximize EAP performance. Results of an analysis of the energies also shows that adjusting particle arrangements with respect to the field can either promote or inhibit instabilities, which can either cause material failure or be harvested for larger deformations depending on boundary conditions. At the macro-scale, a numerical model was developed for arbitrarily segmented and multi-layered beams utilizing EAPs and MAEs. The model was employed in a multi-objective design optimization problem to minimize shape error and cost, while maximizing work output. Optimization results emphasized the importance of gaps between MAE patches and the uniformity and symmetry in their magnetization for matching symmetric shapes with ideal folds. Furthermore, for greatest work performance, some patches yielded greater sensitivity than others, offering a trade-off between work and shape approximation. While these results could be improved with a larger and more diverse initial population, the methodology demonstrated the ability to quickly achieve near-optimal designs with a wide selection of designs based on application priorities.