Deep learning-augmented T-junction droplet generation.

ABSTRACT: Droplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a

SUBMITTER: Ahmadpour A 

PROVIDER: S-EPMC10951907 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature