Topology Optimization of Porous Electrodes using Scalable Modeling Approaches

Redox flow batteries (RFBs) are a class of rechargeable electrochemical systems that are particularly promising for grid-level electricity storage.

Integrating the advantages of RFBs with metal-air batteries results in hybrid metal-air RFBs, having great potential to unlock ultra-low-cost energy storage if certain efficiency issues are resolved.

In these systems, porous electrodes are performance-defining components affecting the thermodynamics, kinetics, and transport phenomena.

This project proposes to develop a computational framework of metal-air RFBs to optimize the architecture (topology, morphology, and microstructure) of the porous electrodes.

This proposal is the first to implement a multi-scale computational model of metal-air RFBs for accelerating the optimization of the power and electrochemical efficiency of these systems by considering the up-scaling and manufacturability of the optimized designs.

This will be achieved by developing high-performance physics-based computational models of metal-air RFB processes in different length scales and employing them in inverse design methodologies for topology optimization of 3D porous electrodes.

The up-scaling and prototyping strategies will be based on triply periodic minimal surface (TPMS) metamaterial design principles that can for the first time produce high-resolution multi-scale and anisotropic designs of the porous electrodes.

Prototypes will be produced using stereolithography 3D printing followed by carbonization to assess the performance of the inversely designed electrodes across different operating conditions.

Verification, validation, and uncertainty quantification (VVUQ) approaches will be used to examine the validity of the multi-scale models.

Moreover, the project considers moving towards the exascale computing paradigm by leveraging proper high-performance computing (HPC) techniques, enabling the models to simulate large-scale RFB systems more accurately in high resolution.