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A Generalized Reduced Fluid Dynamic Model for Redox Flow Batteries

Ziqiang Cheng

A Generalized Reduced Fluid Dynamic Model for Redox Flow Batteries.

Rel. Vittorio Verda. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering), 2021


Redox flow battery (RFB) is considered as one of the most promising stationary energy storage technologies with the potential of commercialization, which might contribute to the deployment of intermittent renewable energies worldwide. To overcome critical technical challenges that still prevent the further market penetration of this technology, e.g., relatively low power and energy density and high pumping losses, many recent researches are concentrated on optimizations of the relevant components, in particular, the electrode and flow field. However, laboratory testing is often time- and material-intensive, and sometimes can be unavailable. Thus numerical simulations are increasingly expected to be an effective way to perform analysis and give in-depth insights on the physical phenomena. Nevertheless, numerical simulations are not without cost. High dimensional models typically require a large computational overhead for multiphysics applications, as compared to their lower dimensional counterparts. Herein, we develop a modeling framework to capture the through-plane fluid dynamic response of a redox flow battery, generating a computationally inexpensive two-dimensional model. We leverage a depth averaging approach that also accounts for variations in the out-of-plane fluid motion and departures from Darcy's law that arises from pivoting from three-dimensions. The results of our 2D model successfully predict the fluid dynamic response of any arbitrary in-plane flow field geometries with reasonable discrepancy of < 5% for both maximum velocity magnitude and pressure drop if not considering the quadratic Forchheimer effect. This corresponds to cheaper computational resources than 3D models (< 1% of duration and 10% of RAM usage) across diverse cell geometries. This methodology can be affixed with further physics, providing a platform for computational reactor optimization.

Relators: Vittorio Verda
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 91
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/20087
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