Downloads provided by UsageCounts
One of the primary challenges in fuel cell stack models, is the lack of numerical methods and computational resources available to handle a full-scale stack geometry to at least the same grid resolution and detailed physics as could be obtained in an equivalently detailed single cell model. To help resolve this challenge, a 3D modelling approach is proposed, and applied to a 3-cell Flowing Electrolyte-Direct Methanol Fuel Cell (FE-DMFC) short-stack. In this approach, the flow fields, backing layers and membranes are solved numerically in a 3D manner, whereas the electrochemical performance is solved analytically. This approach allowed for the detailed physics to be incorporated into the model without the requirement of a high mesh density within the MEA, thus softening the computational load. The methanol concentration distributions at the anode catalyst layer of each cell for different methanol concentrations and sulfuric acid flow rates were found. In addition, the changes of crossover current density and cathodic activation polarization with current density were assessed. The model was used to shed light onto the mechanisms that lead to non-uniform flow behaviour within the stack’s cells; help identify methods to maintain a uniform flow and concentration distribution within the stack; and to provide methods to minimize methanol crossover to the cathode. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 661579. Project Name: Development of a High Performance Flowing Electrolyte-Direct Methanol Fuel Cell Stack Through Modeling and Experimental Studies Acronym: FEDMFC Publication date: 2017-08-31
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
| views | 5 | |
| downloads | 6 |

Views provided by UsageCounts
Downloads provided by UsageCounts