Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics
Polymer dielectrics are promising for high-density energy storage but dielectric breakdown is poorly understood. Here, a phase-field model is developed to investigate electric, thermal, and mechanical effects in the breakdown process for a range of polymer dielectrics, and analytical expression for...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/b2841d10e9d64efca26f0c27e864440d |
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| Sumario: | Polymer dielectrics are promising for high-density energy storage but dielectric breakdown is poorly understood. Here, a phase-field model is developed to investigate electric, thermal, and mechanical effects in the breakdown process for a range of polymer dielectrics, and analytical expression for breakdown strength is provided by machine learning. |
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