Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks

Our most recent publication “Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks”, introduces a groundbreaking methodology for reconstructing fluid behavior using physics-informed neural networks (PINNs) and field data.

PINNs are capable of incorporating physics laws during training, and the proposed methodology addresses the lack of time resolution in experimental applications (such as snapshot particle image velocimetry) by feeding PINNs with time-resolved fields estimated from non-time-resolved field measurements and time-resolved point-probe measurements. This innovative approach not only enhances the precision of data but also allows for the extraction of additional derived quantities, such as pressure distribution. We validate the method on a synthetic test case -fluidic pinball- and a experimental scenario (wake of a wing profile), demonstrating its effectiveness in characterizing fluid flow. The methodology’s potential for broader applications beyond the specific cases studied is evident, making it a significant contribution to the field of fluid mechanics and data-driven modeling. Overall, this article presents a powerful and versatile framework that bridges the gap between experimental limitations and the need for high-fidelity flow characterization.

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Join us: Open PhD position

Join us: Open PhD position

We invite you to apply to the following PhD position, that opened up in the framework of NEXTFLOW. Will you be our next team member? Description and objectives: Turbulent flow control has been achieved in many relevant configurations using both model-free and...

Join us: open Postdoctoral position

Join us: open Postdoctoral position

Our research group invites you to apply for one Postdoctoral position in data-driven flow control at Universidad Carlos III de Madrid. Offer Description Learning lessons from laboratory experiments is not straightforward. The most recent successful histories of flow...

NEXTFLOW at the ISPIV 2023

NEXTFLOW at the ISPIV 2023

The NEXTFLOW team contributed 4 oral presentations at the last 15th International Symposium on Particle Image Velocimetry, held in San Diego (USA). The contributions covered several outcomes of the project, including resolution enhancement and flow estimation from...