Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets.

ABSTRACT: Packed columns are commonly used in post-combustion processes to capture CO₂ emissions by providing enhanced contact area between a CO₂-laden gas and CO₂-absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid-gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO₂-capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost. In contrast, data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. We build our surrogates using MeshGraphNets (MGN), a graph neural netwo

SUBMITTER: Lei B 

PROVIDER: S-EPMC11752894 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature