Neural Topology Optimization Via Active Learning for Efficient Channel Design in Turbulent Mass Transfer.

ABSTRACT: The design of fluid channel structures of reactors or separators of chemical processes is a key to enhancing the mass transfer processes inside the devices. However, systematically designing channel topological structures remains challenging for complex turbulent flows. Here, this challenge is addressed by developing a machine learning framework to efficiently perform topology optimization of channel structures for turbulent mass transfer. A topological structure is represented using a neural network (referred to as "neural topology"), which is optimized by employing pre-trained neural operators combined with a fine-tuning strategy with active data augmentation. The optimization is performed targeting two objectives: maximization of mass transfer efficiency and minimization of energy consu

SUBMITTER: Kou C 

PROVIDER: S-EPMC12499509 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature