A team of scientists led by University of New South Wales researchers Ryan Armstrong, Chuan Zhao and Quentin Meyer has developed a new algorithm to improve the understanding of what is happening inside proton exchange membrane fuel cells.
The PEMFC domain generated by Wang et al.: (a) 2D and (b) 3D rendering of the segmented membrane electrode assembly with artificially overlayed flow channels; the gas channel and land contacting the microporous gas diffusion layer are labeled. Image credit: Wang et al., doi: 10.1038/s41467-023-35973-8.
Proton exchange membrane fuel cells (PEMFCs) use hydrogen fuel to generate electricity and are a quiet, and clean energy source that can power homes, vehicles, and industries.
These fuel cells convert the hydrogen, via an electrochemical process, into electricity with the only by-product of the reaction being pure water.
However, the PEMFCs can become inefficient if the water cannot properly flow out of the cell and subsequently ‘floods’ the system.
Until now, it has been very hard for engineers to understand the precise ways in which water drains, or indeed pools, inside the fuel cells due to their very small size and very complex structures.
The new solution allows for deep learning to create a detailed 3D model by utilizing a lower-resolution X-ray image of the cell, while extrapolating data from an accompanying high-resolution scan of a small sub-section of it.
In more basic terms, it’s the equivalent of taking a blurry aerial photo of an entire town from an aeroplane, along with a very detailed photo of just a few streets, and then being able to accurately predict the lay-out of every road in the entire area.
“One of the reasons this research is so novel is that we are pushing the limit of what can be produced from imaging,” Professor Armstrong said.
“It is very typical that when you use a piece of hardware, whether it’s a microscope or a CT scanner, the resolution of an image gets worse the more you zoom out.”
“Our machine learning technique resolves that problem, and the methodology is broadly applicable where any imaging is taking place, such as medical applications, or the oil and gas industry, or chemical engineering.”
“We have done preliminary super-resolution work with radiologists previously and we could surmise that by obtaining a higher resolution image from a larger field of view that it may be possible to diagnose diseases, such as tumor cells, earlier, when they are smaller.”
The team’s super-resolution algorithm, called DualEDSR, improves the field of view by around 100 times compared to the high-resolution image.
During training and testing, the algorithm achieved 97.3% accuracy when producing high-resolution modeling from low-resolution imagery.
It also produced a high-resolution model in just 1 hour, compared to the 1188 hours (the equivalent of 50 days non-stop) it would have taken to obtain high-resolution images of the whole section of the fuel cell using a micro-CT scanner.
“From our model, we can quickly and precisely see where the water tends to accumulate and therefore, we can help to solve those problems in future designs,” Dr. Meyer said.
“Within the industry it is known that there is a huge untapped performance improvement that could be made using these cells, just by improved water management, and that is estimated to be a 60% increase overall.”
“For the past 20 years, up until now, it has been very hard to have an accurate model of these fuel cells because of the complexity of both the materials, and the way gases and liquids are transported, as well as the electrochemical reactions taking place.”
“Our cross-disciplinary team has enabled us to do just that, bringing so many different expertises to the table. That’s what research is about.”
The team’s work appears in the journal Nature Communications.
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Y.D. Wang et al. 2023. Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning. Nat Commun 14, 745; doi: 10.1038/s41467-023-35973-8
