3 citations · 8 across the 10 of their papers we have counts for
10 papers · 1 filter
Unveiling wing turbulence dynamics through explainable deep learning
Samuel Molina-Casino, Andrés Cremades, Sergio Hoyas +3
The three-dimensional organization of coherent structures in the turbulent flow over aircraft wings remains elusive, limiting our ability to reduce fuel consumption. Here, we use e…
The near-wall cycle for skin-friction generation revealed through explainable deep learning
Andres Cremades, Sergio Hoyas, Ricardo Vinuesa
Skin friction in wall-bounded turbulence is produced by intermittent near-wall motions, yet conventional coherent-structure definitions do not identify which individual events gene…
Explainable deep learning reveals the physical mechanisms behind the turbulent kinetic energy equation
Francisco Alcántara-Ávila, Andrés Cremades, Sergio Hoyas +1
In this work, we investigate the physical mechanisms governing turbulent kinetic energy transport using explainable deep learning (XDL). An XDL model based on SHapley Additive exPl…
X-CAL: Explaining latent causality in physical space for fluid mechanics
Marcial Sanchis-Agudo, Andrés Cremades, Alvaro Martinez-Sanchez +2
We present X-CAL, a pipeline that combines a -variational autoencoder (-VAE) with the synergistic-unique-redundant decomposition (SURD)~\cite{surd} approach for causality ana…
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades +8
Rapid urbanization demands efficient monitoring of turbulent wind and pollutant dispersion, yet existing reconstruction and sensor placement strategies fail under realistic sparsit…
Improving turbulence control through explainable deep learning
Miguel Beneitez, Andres Cremades, Luca Guastoni +1
Turbulent-flow control aims to develop strategies that effectively manipulate fluid systems, such as the reduction of drag in transportation and enhancing energy efficiency, both c…
Assessment of non-intrusive sensing in wall-bounded turbulence through explainable deep learning
A. Cremades, R. Freibergs, S. Hoyas +3
In this work we present a framework to explain the prediction of the velocity fluctuation at a certain wall-normal distance from wall measurements with a deep-learning model. For t…
Classically studied coherent structures only paint a partial picture of wall-bounded turbulence
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood. Previous research has focused on analyzing th…
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, a…
Identifying regions of importance in wall-bounded turbulence through explainable deep learning
Andres Cremades, Sergio Hoyas, Rahul Deshpande +8
Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One o…