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Machine learning control optimises the convective heat transfer on a flat plate

We are excited to announce the publication of our new paper «Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer» in the Journal Applied Thermal Engineering.

Our work uses an artificial intelligence approach based on linear genetic algorithms control to enhance convective heat transfer in a turbulent boundary layer on a flat plate. Six slot jets in crossflow are used as an actuator to define an open-loop optimal periodic forcing using carrier frequency, duty cycle, and phase difference as control parameters. The control laws are optimized with respect to the unperturbed boundary layer and steady jet actuation. This experimental investigation highlights the potential of machine learning control and the feasibility of using advanced algorithms with sophisticated measurement techniques.

To learn more, visit this link.

This work has been supported by the project ARTURO, ref. PID2019-109717RB-I00/AEI/10.13039/501100011033, funded by the Spanish State Research Agency.

S. Discetti also acknowledges the funding provided by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 949085, NEXTFLOW)

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Here we are!

Here we are!

Welcome to the webpage of the ARTURO project. While it is still under construction, you can already find information about the project and the team using the menu at the top of the page. Enjoy!