This work proposes a new machine learning (ML)-assisted coupled numerical method to elucidate multi-scale reactive transport behaviors within redox flow battery (RFB) electrodes, spanning from quantum to device scales. Leveraging the key quantum-scale mechanisms provided by density functional theory calculations, kinetic Monte Carlo (KMC) method is employed to resolve the coupled interfacial reaction kinetics and ions transport within electric double layer, thereby fundamentally bridging the RFB’s reaction mechanisms and complex transport phenomena at microscopic scale. By collecting the dataset generated from KMC simulation cases, an ML model is used to predict the interfacial current/mass flux at the complicated potential and species concentration conditions. Then, the trained ML model is integrated into a mesoscopic lattice Boltzmann (LB) model for establishing the pore-scale reactive transport model. Moreover, the bidirectional LB-finite element cross-scale computational framework is further built for linking mesoscopic and macroscopic numerical methods. This multi-scale framework investigates the reactive transport characteristics of negative-side electrode during all-vanadium RFB galvanostatic charging process. The numerical results reveal that: At the macroscopic scale, avoiding localized low-velocity dead zones is key to optimizing reaction and transport processes during practical RFB operation; At the mesoscopic scale, the disordered and heterogeneous pore structures within the electrode induce an imbalance in reaction and transport behavior, thereby impacting the RFB's macroscopic performance. Overall, this work delivers a cross-scale insight into the coupled reaction and transfer mechanism of RFBs, thereby facilitating the synergistic design optimization of multi-scale electrode morphology.
 

Redox flow battery,Multi-scale model,Kinetic Monte Carlo,Lattice Boltzmann simulation