A generalized reduced fluid dynamic model for flow fields and electrodes in redox flow batteries

High-dimensional models typically require a large computational overhead for multiphysics applications, which hamper their use for broad-sweeping domain interrogation. Herein, we develop a modeling framework to capture the through-plane fluid dynamic response of electrodes and flow fields in a redox flow cell, generating a computationally inexpensive two-dimensional (2D) model. We leverage a depth-averaging approach that also accounts for variations in out-of-plane fluid motion and departures from Darcy's law that arise from averaging across three-dimensions (3D). Our resulting depth-averaged 2D model successfully predicts the fluid dynamic response of arbitrary in-plane flow field geometries, with discrepancies of <5% for both maximum velocity and pressure drop. This corresponds to reduced computational expense, as compared to 3D representations (<1% of duration and 10% of RAM usage), providing a platform to screen and optimize a diverse set of cell geometries

Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods

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 2D model 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 τ 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 stacks