Computational and Data-Driven Modeling of Advanced Piezoelectric Composites: From Microstructure to Device Performance
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 31 August 2027 | Viewed by 128
Editors
Interests: advanced manufacturing; data-driven modeling; battery modeling
Interests: materials and modeling; additive manufacturing; biomedical devices
Special Issue Information
Dear Colleagues,
Piezoelectric composites are central to a rapidly expanding set of technologies, including ultrasonic transducers, biomedical imaging probes, vibration energy harvesters, structural health monitoring sensors, underwater acoustics, and emerging soft and flexible electronics. By combining two or more constituent phases, for example, piezoceramic rods, fibers, or particles dispersed in a polymer matrix, these materials achieve combinations of electromechanical coupling, compliance, and acoustic impedance that single-phase materials cannot deliver. The mapping from constituent properties, microstructural architecture, processing-induced defects, and operating conditions to macroscopic device performance is highly nonlinear and remains incompletely understood. Computational modeling has therefore become indispensable for the design, optimization, and reliability assessment of advanced piezoelectric composites across multiple length and time scales.
This Special Issue aims to bring together recent advances in computational and data-driven modeling of advanced piezoelectric composites, spanning from microstructure to device performance. We welcome contributions employing classical numerical methods, including finite element analysis, phase-field simulation, homogenization, and multiphysics modeling, alongside emerging machine learning, deep learning, and generative approaches that are transforming how piezoelectric composites are discovered, optimized, and deployed. Original research articles, comprehensive reviews, and perspectives on future directions are all welcome.
Specific topics of interest include, but are not limited to the following:
- Microstructure–property relationships in 0–3, 1–3, 2–2, and hierarchical piezoelectric composites;
- Multiscale and multiphysics modeling of coupled electromechanical behavior;
- Phase-field simulation of domain switching, fatigue, and degradation;
- Machine learning and deep learning for property prediction, microstructure generation, and inverse design;
- Topology and generative optimization of composite architectures;
- Surrogate modeling and uncertainty quantification for piezoelectric devices;
- Modeling of flexible, soft, and lead-free piezoelectric composites;
- Digital twins linking simulation, manufacturing, and device performance;
- High-throughput simulation coupled with experimental validation.
Dr. Wenhua Yang
Dr. Fan Fei
Dr. Li He
Guest Editors
Manuscript Submission Information
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Keywords
- piezoelectric composites
- machine learning
- deep learning
- inverse design
- microstructure–property relationships
- multiscale modeling
- phase-field simulation
- lead-free piezoelectrics
- energy harvesting
- flexible electronics
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