The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.
Semi‐automated Data‐driven FE Mesh Generation and Inverse Parameter Identification for a Multiscale and Multiphase Model of Function‐Perfusion Processes in the Liver
