The Higher Technical School of Engineering (ETSi) has awarded the ETSi Scientific Article Prize for the October-December 2024 Term, jointly to the following works:
*  "A numerical study on heat transfer for serpentine-type cooling channels in a PEM fuel cell stack", Energy, vol. 307, October 2024, pp. 132634. DOI: 10.1016/j.energy.2024.132634, developed by Laura González-Morán, Christian Suárez, Alfredo Iranzo, Lei Han and Felipe Rosa
* “eFFT: An Event-Based Method for the Efficient Computation of Exact Fourier Transforms", IEEE Transactions on Pattern Analysis and Machine Learning, vol. 46, December 2024, pp. 9630-9647. DOI: 10.1109/TPAMI.2024.3422209, developed by Raúl Tapia, José Ramiro Martínez de Dios and Aníbal Ollero.

"A numerical study on heat transfer for serpentine-type cooling channels in a PEM fuel cell stack"

The purpose of this study is to numerically analyze heat transfer in coil-type cooling channels in PEM fuel cell stacks. The effects of coolant type, flow rate, inlet temperature, contact thermal resistance, gas diffusion layer, and bipolar plate material were studied using CFD simulations in a cell with an active area of ​​100 m². A new correlation for the Nusselt number was developed. The results of the study indicate that coolant flow rate and the thermal conductivity of the bipolar plate have the greatest impact on cooling capacity. It was concluded that for Uniform Temperature Index (UTI) values ​​above 3.65%, temperature differences across the membrane exceed 5 K, which could cause serious degradation problems.

The competition jury highlighted the high quality of the submitted publications, emphasizing both the scientific rigor of the journals in which they were published and the significance of the citations received by the various works. In this context, and after applying several bibliometric indicators, the winning work was unanimously selected for its theoretical contribution to the field of engineering.

This recognition is part of the ETSi's effort to promote and highlight excellence in the scientific production of its research community.

 “eFFT: An Event-Based Method for the Efficient Computation of Exact Fourier Transforms”

This work introduces eFFT, the first method capable of accurately calculating the Fourier transform of an asynchronous event stream (data generated by neuromorphic cameras) in real time without converting it into conventional images. The key is a data tree that keeps the Radix-2 decomposition of the FFT updated: only the nodes affected by each new event or event packet are recalculated, reusing the rest of the results and avoiding redundant operations.
Main results
: - Numerical accuracy: PSNR >57dB and SSIM >0.9.
- Speed: on the order of microseconds per event at 128x128 resolution.
- Online execution on limited hardware without data loss.
- Open source for the research community.
Impact
: - Enables frequency processing of event cameras in embedded systems with very low latency.
- Proven in tasks such as noise reduction, object tracking, visual recording, and scene classification.
- Facilitates new neuromorphic vision algorithms in a wide range of applications.