Digital twins for fluid sloshing problems

Teaching robots to manipulate liquids (to help elderly people, for instance) is a challenge. We are used to recognize different fluids at first sight, but this educated intuition is difficult to transmit to robots.

Our last job has been just published in the Public Library of Science (PLOS)ONE journal (Open Access):

We present a #digitaltwin for fluid sloshing that, through computer vision and making an extensive use of simulation in the background, is able to learn the particular fluid and its behavior—even if the robot has not been exposed to it before—and predict its dynamics in the near future.

A great job by BEATRIZ MOYA, David González, Iciar Alfaro, and Francisco Chinesta, under the financial support from ESI Group, which is gratefully acknowledged.

AMB highly cited papers

Three of our group’s papers are among the 1% most cited papers of their respective specialities.

These papers are:

  1. PGD-based computational vademecum for efficient design, optimization and control. F. Chinesta, A. Leygue, F. Bordeu, J.V. Aguado, E. Cueto, D. Gonzalez, I. Alfaro, A. Ammar, A. Huerta. Archives of Computational Methods in Engineering, 20(1), 31-59, 2013. [Download pdf of draft]
  2. A short review on model order reduction based on Proper Generalized Decomposition. F. Chinesta, P. Ladeveze, E. Cueto. Archives for Numerical Methods in Engineering, 18, 395-404, 2011. [Download pdf of draft]
  3. Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models. Francisco Chinesta, Amine Ammar, Elias Cueto. Archives for Numerical Methods in Engineering, 17(4), 327-350, 2010. [download pdf of draft]

They are also among the few papers from our university in this list.