Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics.

ABSTRACT: Understanding the breakdown mechanisms of polymer-based dielectrics is critical to achieving high-density energy storage. Here a comprehensive phase-field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer-based dielectrics. High-throughput simulations are performed for the P(VDF-HFP)-based nanocomposites filled with nanoparticles of different properties. Machine learning is conducted on the database from the high-throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF-HFP)-based nanocomposites. The present work provides fundamental insights to the breakdown m

SUBMITTER: Shen ZH 

PROVIDER: S-EPMC6478924 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature