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.
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…
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