Computational Facilitation of Large Scale Microfluidic Fuel Cell Architectures

TL;DR

This paper introduces a reduced-order simulation method for large-scale microfluidic fuel cell architectures, enabling efficient modeling and design of scalable systems beyond traditional CFD limitations.

Contribution

A novel reduced-order modeling approach that accurately simulates microfluidic fuel cell stacks with significantly less computational effort.

Findings

The method closely matches detailed CFD results.

It significantly reduces simulation time for large systems.

Supports design of macroscale microfluidic fuel cell systems.

Abstract

Hydrogen fuel cells are a key technology in the transition toward carbon-neutral energy systems, offering clean power with water as the only byproduct. Microfluidic fuel cells, which operate at the microliter scale, are an emerging variant that offer fine control over fluid and thermal dynamics, along with compact, efficient designs. However, scaling these systems to meet practical power demands remains a major challenge -- particularly due to the limitations of conventional simulation methods like Computational Fluid Dynamics (CFD), which are computationally expensive and scale poorly. In this work, we propose a reduced-order simulation method that models the behavior of individual microfluidic fuel cells and efficiently extends it to large scale stacks. This approach significantly reduces simulation time while maintaining close agreement with detailed CFD results. The method is validated, evaluated for scalability, and discussed in the context of ongoing advancements in microfluidic fuel cell fabrication. The obtained results demonstrate that this abstraction can support the design and development of scalable microfluidic fuel cell systems and, for the first time, the consideration of first macroscale instances of practical value.